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Objective: Social interactions and experiences are increasingly occurring online, including for young adults with psychosis. Healthy social interactions and experiences are widely recognized as a critical component of social recovery, yet research thus far has focused predominantly on offline interactions with limited understanding of these interactions online. We developed the Social Media and Internet sociaL Engagement (SMILE) questionnaire to assess the type, frequency, and nature of online social interactions and experiences among young adults with early psychosis to better assess online social activity and ultimately support personalized interventions., Methods: Participants ( N  = 49) completed the SMILE questionnaire which asked about online platforms used, frequency of use, and if positive and negative experiences were more likely to happen online or offline. Participants completed additional self-report measures of victimization, positive psychotic symptoms, social functioning, and demographics. Exploratory factor analysis and correlations between identified factors and clinical measures of interest were completed., Results: Exploratory factor analysis revealed three factors: positive engagement, victimization, and internalizing experiences. Most participants (6%-37%) experienced positive engagement offline. Victimization occurred equally online and offline (8%-27% and 4%-24%, respectively). Most participants (37%-51%) endorsed internalizing experiences as occurring equally offline and online, but approximately a third of participants reported internalizing experiences more frequently offline (20%-35%). Victimization was moderately (r =  0.34) correlated with overall online social experiences, suggesting more online time may increase the likelihood of victimization. Age was inversely related to the frequency of overall online social experiences., Conclusion: Young adults with early psychosis experience positive and negative social experiences online and offline. New scales and measures to comprehensively assess the nature and function of online social interactions and experiences are needed., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Non-clinical, on-demand peer-to-peer (PtP) support apps have become increasingly popular over the past several years. Although not as pervasive as general self-help apps, these PtP support apps are usually free and instantly connect individuals through live texting with a non-clinical volunteer who has been minimally trained to listen and offer support. To date, there is little empirical work that examines whether and how using an on-demand PtP support app improves emotional well-being. Applying regression and multilevel models to N  = 1000+ PtP conversations, this study examined whether individuals experience emotional improvement following a conversation on a PtP support app (HearMe) and whether dyadic characteristics of the conversation - specifically, verbal and emotional synchrony - are associated with individuals' emotional improvement. We found that individuals reported emotional improvement following a conversation on the PtP support app and that verbal (but not emotional) synchrony was associated with the extent of individuals' emotional improvement. Our results suggest that online PtP support apps are a viable source of help. We discuss cautions and considerations when applying our findings to enhance the delivery of support provision on PtP apps.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

Background and Hypothesis: Longer duration of untreated psychosis (DUP) predicts worse outcomes in First Episode Psychosis (FEP). Searching online represents one of the first proactive step toward treatment initiation for many, yet few studies have informed how best to support FEP youth as they engage in early online help-seeking steps to care., Study Design: Using a stepped-wedge randomized design, this project evaluated the effectiveness of a digital marketing campaign at reducing DUP and raising rates of referrals to FEP services by proactively targeting and engaging prospective patients and their adult allies online., Study Results: Throughout the 18-month campaign, 41 372 individuals visited our website, and 371 advanced to remote clinical assessment (median age = 24.4), including 53 allies and 318 youth. Among those assessed (n = 371), 53 individuals (14.3%) reported symptoms consistent with psychotic spectrum disorders (62.2% female, mean age 20.7 years) including 39 (10.5%) reporting symptoms consistent with either Clinical High Risk (ie, attenuated psychotic symptoms; n = 26) or FEP (n = 13). Among those with either suspected CHR or FEP (n = 39), 20 (51.3%) successfully connected with care. The campaign did not result in significant differences in DUP., Conclusion: This study highlights the potential to leverage digital media to help identify and engage youth with early psychosis online. However, despite its potential, online education and professional support alone are not yet sufficient to expedite treatment initiation and reduce DUP., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective: Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker., Methods: In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design., Results: The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems., Conclusions: This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry., Competing Interests: Dr. Gallego has served as a speaker for Tecnoquimicas. Dr. Rubio has served as a consultant for Janssen, Karuna, and TEVA, has received research funding from Alkermes, and has received royalties from UpToDate. Dr. Birnbaum has served as a consultant for Northshore Therapeutics and HearMe. Dr. Robinson has served as a consultant for Acadia, Advocates for Human Potential, Amalyx, APA, C4 Innovations, Costello Medical Consulting, Health Analytics, Innovative Science Solutions, Janssen, Lundbeck, Neurocrine, Neuronix, Otsuka, Teva, and US WorldMeds and has received grant support from Otsuka. Dr. Malhotra has served as a consultant for Acadia Pharma, Clarivate, Genomind, Health Advances, InformedDNA, Iqvia, and Janssen Pharma. The other authors report no financial relationships with commercial interests.

Background: Cognitive impairment is integral to the pathophysiology of psychosis. Recent findings implicate autonomic arousal-related activity in both momentary fluctuations and individual differences in cognitive performance. Although altered autonomic arousal is common in patients with first-episode psychosis (FEP), its contribution to cognitive performance is unknown., Methods: A total of 24 patients with FEP (46% male, age = 24.31 [SD 4.27] years) and 24 control subjects (42% male, age = 27.06 [3.44] years) performed the Multi-Source Interference Task in-scanner with simultaneous pulse oximetry. First-level models included the cardiac-blood oxygen level-dependent regressor, in addition to task (congruent, interference, and error) and nuisance (motion and CompCor physiology) regressors. The cardiac-blood oxygen level-dependent regressor reflected parasympathetic arousal-related activity and was created by convolving the interbeat interval at each heartbeat with the hemodynamic response function. Group models examined the effect of group or cognitive performance (reaction times × error rate) on arousal-related and task activity, while controlling for sex, age, and framewise displacement., Results: Parasympathetic arousal-related activity was robust in both groups but localized to different regions for patients with FEP and healthy control subjects. Within both groups, arousal-related activity was significantly associated with cognitive performance across occipital and temporal cortical regions. Greater arousal-related activity in the bilateral prefrontal cortex (Brodmann area 9) was related to better performance in healthy control subjects but not patients with FEP., Conclusions: Autonomic arousal circuits contribute to cognitive performance and the pathophysiology of FEP. Arousal-related functional activity is a novel indicator of cognitive ability and should be incorporated into neurobiological models of cognition in psychosis., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature., Objective: Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers' training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms., Methods: Windowed timeline data of patients with a diagnosis of schizophrenia spectrum disorder before a known hospitalization event and healthy controls were gathered from 3 platforms: Facebook (254/268, 94.8% of participants), Twitter (51/268, 19% of participants), and Instagram (134/268, 50% of participants). We then used a 3 × 3 combinatorial binary classification design to train machine learning classifiers and evaluate their performance on testing data from all available platforms. We further compared results from models in intraplatform experiments (ie, training and testing data belonging to the same platform) to those from models in interplatform experiments (ie, training and testing data belonging to different platforms). Finally, we used Shapley Additive Explanation values to extract the top predictive features to explain and compare the underlying constructs that predict hospitalization on each platform., Results: We found that models in intraplatform experiments on average achieved an F 1 -score of 0.72 (SD 0.07) in predicting a psychiatric hospitalization because of schizophrenia spectrum disorder, which is 68% higher than the average of models in interplatform experiments at an F 1 -score of 0.428 (SD 0.11). When investigating the key drivers for divergence in construct validities between models, an analysis of top features for the intraplatform models showed both low predictive feature overlap between the platforms and low pairwise rank correlation (<0.1) between the platforms' top feature rankings. Furthermore, low average cosine similarity of data between platforms within participants in comparison with the same measurement on data within platforms between participants points to evidence of identity fragmentation of participants between platforms., Conclusions: We demonstrated that models built on one platform's data to predict critical mental health treatment outcomes such as hospitalization do not generalize to another platform. In our case, this is because different social media platforms consistently reflect different segments of participants' identities. With the changing ecosystem of social media use among different demographic groups and as web-based identities continue to become fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required., (©Viet Cuong Nguyen, Nathaniel Lu, John M Kane, Michael L Birnbaum, Munmun De Choudhury. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.12.2022.)

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment., (© 2022. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Background: Digital technology has the potential to transform psychiatry, but its adoption has been limited. The proliferation of telepsychiatry during the COVID-19 pandemic has increased the urgency of optimizing technology for clinical practice. Understanding clinician attitudes and preferences is crucial to effective implementation and patient benefit., Objective: Our objective was to elicit clinician perspectives on emerging digital technology., Methods: Clinicians in a large psychiatry department (inpatient and outpatient) were invited to complete a web-based survey about their attitudes toward digital technology in practice, focusing on implementation, clinical benefits, and expectations about patients' attitudes. The survey consisted of 23 questions that could be answered on either a 3-point or 5-point Likert scale. We report the frequencies and percentages of responses., Results: In total, 139 clinicians completed the survey-they represent a variety of years of experience, credentials, and diagnostic subspecialties (response rate 69.5%). Overall, 83.4% (n=116) of them stated that digital data could improve their practice, and 23.0% (n=32) of responders reported that they had viewed patients' profiles on social media. Among anticipated benefits, clinicians rated symptom self-tracking (n=101, 72.7%) as well as clinical intervention support (n=90, 64.7%) as most promising. Among anticipated challenges, clinicians mostly expressed concerns over greater time demand (n=123, 88.5%) and whether digital data would be actionable (n=107, 77%). Furthermore, 95.0% (n=132) of clinicians expected their patients to share digital data., Conclusions: Overall, clinicians reported a positive attitude toward the use of digital data to not only improve patient outcomes but also highlight significant barriers that implementation would need to overcome. Although clinicians' self-reported attitudes about digital technology may not necessarily translate into behavior, our results suggest that technologies that reduce clinician burden and are easily interpretable have the greatest likelihood of uptake., (©William Andrew Sterling, Michael Sobolev, Anna Van Meter, Daniel Guinart, Michael L Birnbaum, Jose M Rubio, John M Kane. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.11.2022.)

The goals of conducting disaster research are to obtain information to: (1) decrease the human, environmental, and economic losses; (2) decrease morbidity; (3) decrease pain and suffering; and (4) enhance the recovery of the affected population. Two principal, but inter-related, branches of disaster research are: (1) Epidemiological; and (2) Interventional. In response to the need for the discipline of disaster health to build its science on data that are generalizable and comparable, a set of five Frameworks have been developed to structure the information and research of the health aspects of disasters: (1) Conceptual; (2) Longitudinal; (3) Transectional Societal; (4) Relief-Recovery; and (5) Risk-Reduction. These Frameworks provide a standardized format for studying and comparing the epidemiology of disasters as well as evaluating the interventions (responses) provided prior to, during, and following a disaster, especially as they relate to the health status of the people affected or at-risk. Critical to all five Frameworks is the inclusion of standardized definitions of the terms used to describe factors that lead to and affect the occurrence and severity of a disaster. The Conceptual Framework describes the progression of a hazard that becomes an event, which causes structural damage and a decrease or loss of function (functional damage), that, in turn, produces needs that lead to a disaster. The Longitudinal Framework describes this chronological progression as phases in order of their appearance in time, even though some of them occur concurrently. In order to study and compare the effects of an event on the complex amalgam that constitutes a society, the essential functions of a society have been deconstructed into 13 Basic Societal Systems that comprise the Transectional Societal Framework. These diverse, but inter-related Basic Societal Systems interface with each other through a 14th system called Coordination and Control. Epidemiological research studies the relationships and occurrences that influence and result from a disaster. Interventional research involves the evaluation of interventions, whether they are directed at relief, recovery, hazard mitigation, capacity building, or performance. The Relief-Recovery and Risk-Reduction Frameworks are based on a Disaster Logic Model. The Relief-Recovery Framework provides the structure necessary to systematically evaluate specific interventions provided during the Relief and Recovery phases of a disaster. The Risk-Reduction Framework details the processes involved in mitigating the risk that a hazard will produce a destructive event and/or that capacity building will augment the resilience of a community to the consequences of such an event. It incorporates a cascade of risks that lead from the presence of a hazard to the development of a disaster. Risk is described as the likelihood that each of the steps leading from a hazard to a disaster will take place; it also includes the probable consequences of the occurrence of each of the elements in the Conceptual Framework. The Conceptual, Longitudinal, and Transectional Societal Frameworks are useful in epidemiological research, i.e., the study of the incidence of, and factors influencing events and disasters. The Relief-Recovery and Risk-Reduction Frameworks are added to the Conceptual, Longitudinal, and Transectional Societal Frameworks for conducting and reporting of interventional research/evaluations. Examples of the application of these Frameworks are provided.

Background: Mental illness in transition age youth is common and treatment initiation is often delayed. Youth overwhelmingly report utilizing the Internet to gather information while psychiatric symptoms emerge, however, most are not yet ready to receive a referral to care, forestalling the established benefit of early intervention., Methods: A digital outreach campaign and interactive online care navigation platform was developed and deployed in New York State on October 22, 2020. The campaign offers live connection to a peer or counselor, a self-assessment mental health quiz, and educational material all designed to promote help-seeking in youth and their allies., Results: Between October 22, 2020 and July 31, 2021, the campaign resulted in 581,981 ad impressions, 16,665 (2.9%) clicks, and 13,717 (2.4%) unique website visitors. A third (4,562, 33.2%) completed the quiz and 793 (0.1%) left contact information. Of those, 173 (21.8%) completed a virtual assessment and 155 (19.5%) resulted in a referral to care. The median age of those referred was 21 years (IQR = 11) and 40% were considered to be from low-income areas. Among quiz completers, youth endorsing symptoms of depression or anxiety were more likely to leave contact information (OR = 2.18, 95% CI [1.39, 3.41] and OR = 1.69, 95% CI [1.31, 2.19], respectively) compared to those not reporting symptoms of depression or anxiety. Youth endorsing symptoms of psychosis were less likely to report a desire to receive a referral to care (OR = 0.58, 95% CI [0.43, 0.80]) compared to those who did not endorse symptoms of psychosis., Conclusion: Self-reported symptomatology impact trajectories to care, even at the earliest stages of help-seeking, while youth and their allies are searching for information online. An online care navigation team could serve as an important resource for individuals with emerging behavioral health concerns and help to guide the transition between online information seeking at baseline to care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Birnbaum, Garrett, Baumel, Germano, Lee, Sosa, Ngo, Fox, Dixon and Kane.)

Background: Several studies have shown the benefits of coordinated specialty care (CSC) for individuals with first episode psychosis; however, pathways to care are marred by lack of knowledge, stigma, and difficulties with treatment engagement. Serious games or video interventions may provide a way to address these factors., Objective: This study focuses on qualitative results of a randomized controlled trial comparing OnTrack>The Game (OTG) with recovery videos (RVs) on engagement, stigma, empowerment, hope, recovery, and understanding of psychosis in clients receiving CSC. Clinicians are also interviewed regarding their perceptions of the interventions and suggestions for improvement., Methods: A total of 16 clients aged 16-30 years, with first episode psychosis attending a CSC program in New York State, and 9 clinicians participated in the qualitative interviews. Interviews were analyzed using the rapid identification of themes from audio recordings method., Results: For clients, themes included relatability of game content, an increased sense of hope and the possibility of recovery, decreased self-stigma and public stigma, increased understanding of the importance of social support, and increased empowerment in the OTG group. Clinicians had a preference for RV and provided suggestions for dissemination and implementation., Conclusions: Themes that may help inform future research in this area, particularly regarding dissemination and implementation of OTG and RV, emerged., Trial Registration: ClinicalTrials.gov NCT03390491; https://clinicaltrials.gov/ct2/show/NCT03390491., (©Samantha Jankowski, Kathleen Ferreira, Franco Mascayano, Effy Donovan, Reanne Rahim, Michael L Birnbaum, Sabrina Yum-Chan, Deborah Medoff, Bethany Marcogliese, Lijuan Fang, Terriann Nicholson, Lisa Dixon. Originally published in JMIR Mental Health (https://mental.jmir.org), 06.04.2022.)

For people diagnosed with mental health conditions, psychiatric hospitalization is a major life transition, involving clinical treatment, crisis stabilization and loss of access of social networks and technology. The period after hospitalization involves not only management of the condition and clinical recovery but also re-establishing social connections and getting back to social and vocational roles for successful reintegration - a significant portion of which is mediated by social technology. However, little is known about how people get back to social lives after psychiatric hospitalization and the role social technology plays during the reintegration process. We address this gap through an interview study with 19 individuals who experienced psychiatric hospitalization in the recent past. Our findings shed light on how people's offline and online social lives are deeply intertwined with management of the mental health condition after hospitalization. We find that social technology supports reintegration journeys after hospitalization as well as presents certain obstacles. We discuss the role of social technology in significant life transitions such as reintegration and conclude with implications for social computing research, platform design and clinical care.

Aim: Cannabis use is common among individuals with first episode psychosis (FEP) and persistent use is associated with worse outcomes. The purpose of this qualitative study is to identify factors pertaining to onset of cannabis use and persistent use among young adults with early psychosis receiving coordinated specialty care (CSC) in the United States and begin to develop a theoretical framework to drive further study and hypothesis testing and inform the approach to treatment of cannabis use disorder in this setting., Methods: Participants were ages 16-30 years with early psychosis attending a CSC program in New York State. Interviews were conducted in December 2018. Coding and analysis was conducted in Atlas.ti and themes were identified via a thematic analysis approach., Results: Thirteen individuals completed the interview. The mean age in years was 20.7 and the majority were male (n = 10). Almost half (46%) were Black, non-Hispanic and 39% were Hispanic. Seven participants indicated they were currently using cannabis and six participants indicated they had stopped for at least 6 months at the time of the interview. Several themes emerged including the influence of family and social norms, motivating factors for persistent use and for reduced use or abstinence, and ambivalence regarding the impact of cannabis use on mental health., Conclusion: A theoretical framework emerged which may help identify future research in this area and inform the approach to treatment of cannabis use disorder in this setting., (© 2021 John Wiley & Sons Australia, Ltd.)

Background: In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions., Objective: We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder., Methods: We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation., Results: The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61)., Conclusions: This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis., (©Michael L Birnbaum, Avner Abrami, Stephen Heisig, Asra Ali, Elizabeth Arenare, Carla Agurto, Nathaniel Lu, John M Kane, Guillermo Cecchi. Originally published in JMIR Mental Health (https://mental.jmir.org), 24.01.2022.)

We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2022 The Author(s).)

Background: Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored., Objective: The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients' social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations., Methods: We developed a prototype that can analyze consented patients' Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient's social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis., Results: Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients' verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients' social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings., Conclusions: Our findings support the touted potential of computational mental health insights from patients' social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology., (©Dong Whi Yoo, Sindhu Kiranmai Ernala, Bahador Saket, Domino Weir, Elizabeth Arenare, Asra F Ali, Anna R Van Meter, Michael L Birnbaum, Gregory D Abowd, Munmun De Choudhury. Originally published in JMIR Mental Health (https://mental.jmir.org), 16.11.2021.)

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., (©Khatiya C Moon, Anna R Van Meter, Michael A Kirschenbaum, Asra Ali, John M Kane, Michael L Birnbaum. Originally published in JMIR Mental Health (https://mental.jmir.org), 22.10.2021.)

Background and Objectives: Prior research has successfully identified linguistic and behavioral patterns associated with schizophrenia spectrum disorders (SSD) from user generated social media activity. Few studies, however, have explored the potential for image analysis to inform psychiatric care for individuals with SSD. Given the popularity of image-based platforms, such as Instagram, investigating user generated image data could further strengthen associations between social media activity and behavioral health. Methods: We collected 11,947 Instagram posts across 68 participants (mean age = 23.6; 59% male) with schizophrenia spectrum disorders (SSD; n = 34) and healthy volunteers (HV; n = 34). We extracted image features including color composition, aspect ratio, and number of faces depicted. Additionally, we considered social connections and behavioral features. We explored differences in usage patterns between SSD and HV participants. Results: Individuals with SSD posted images with lower saturation ( p = 0.033) and lower colorfulness ( p = 0.005) compared to HVs, as well as images showing fewer faces on average ( SSD = 1.5, HV = 2.4, p < 0.001). Further, individuals with SSD demonstrated a lower ratio of followers to following compared to HV participants ( p = 0.025). Conclusion: Differences in uploaded images and user activity on Instagram were identified in individuals with SSD. These differences highlight potential digital biomarkers of SSD from Instagram data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hänsel, Lin, Sobolev, Muscat, Yum-Chan, De Choudhury, Kane and Birnbaum.)

Objective: In this study, the authors aimed to characterize psychoeducation provided to inpatients with first-episode psychosis (FEP) and their families., Methods: Psychiatrists were surveyed about how they provide psychoeducation to this population., Results: In total, 60 psychiatry trainees at nine New York City hospitals responded to the survey invitation. Almost all reported that they provide psychoeducation. Most (81% for patients, 84% for families) reported that psychoeducation content and delivery method were not uniform. The most frequently used delivery method was unstructured conversation (98%), followed by handouts (25% for patients, 26% for families). Responses from a national sample (N=167) revealed similar trends., Conclusions: Most respondents provided some form of psychoeducation to hospitalized patients with FEP and their families. Few utilized a standardized method, and less than one-third incorporated supplemental materials. Inpatient psychoeducation for this population was largely informal, and patients and their families were not receiving consistent content and quality of information.

For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of clinical recovery such as removal of symptoms, and social reintegration involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness - either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples' mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples' trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals' reintegration trajectories post-hospitalization. We discuss the implications of combining peoples' clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.

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.

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., Competing Interests: The authors have declared that no competing interests exist.

Aim: Manually explore the Google search queries of individuals with first episode psychosis prior to their first hospitalization, in effort to identify common themes and search interests during the period of emerging illness., Methods: Individuals hospitalized for psychosis between December 2016 and September 2017 provided access to their Google archive data for manual qualitative evaluation of search content. Searches conducted during the 6-month time period prior to the participant's first hospitalization for psychosis were extracted and evaluated for search activity associated with mental health., Results: Of 20 archives reviewed, 15 individuals (75%) searched for information classified by reviewers as related to mental health. Searches with content associated with delusions were found in 15 participant archives (75%). Searches related to negative symptoms including social withdrawal and decline in function were identified in 6 participant's search archives (30%). Four participants (20%) had searches that were associated with thought processes, and 2 participants (10%) searched for information on suicide. Four participants (20%) searched for information related to anxiety, whereas 3 participants (15%) had searches related to depressive symptoms., Conclusions: Individuals with early psychosis appear to be using the Internet for obtaining information about their early symptoms and experiences prior to their first contact with psychiatric care. Improving our understanding of the ways by which individuals with emerging psychosis search for information about their experiences online may help mental health clinicians tailor online resources in hopes of improving pathways to care and reducing the duration of untreated psychosis., (© 2019 John Wiley & Sons Australia, Ltd.)

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., (©Michael Leo Birnbaum, Prathamesh "Param" Kulkarni, Anna Van Meter, Victor Chen, Asra F Rizvi, Elizabeth Arenare, Munmun De Choudhury, John M Kane. Originally published in JMIR Mental Health (http://mental.jmir.org), 01.09.2020.)

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., (©Dong Whi Yoo, Michael L Birnbaum, Anna R Van Meter, Asra F Ali, Elizabeth Arenare, Gregory D Abowd, Munmun De Choudhury. Originally published in JMIR Mental Health (http://mental.jmir.org), 12.08.2020.)

Parasympathetic arousal is associated with states of heightened attention and well-being. Arousal may affect widespread cortical and subcortical systems across the brain, however, little is known about its influence on cognitive task processing and performance. In the current study, healthy adult participants (n ​= ​20) underwent multi-band echo-planar imaging (TR ​= ​0.72 ​s) with simultaneous pulse oximetry recordings during performance of the Multi Source Interference Task (MSIT), the Oddball Task (OBT), and during rest. Processing speed on both tasks was robustly related to heart rate (HR). Participants with slower HR responded faster on both the MSIT (33% variance explained) and the OBT (25% variance explained). Within all participants, trial-to-trial fluctuations in processing speed were robustly related to the heartbeat-stimulus interval, a metric that is dependent both on the concurrent HR and the stimulus timing with respect to the heartbeat. Models examining the cardiac-BOLD response revealed that a distributed set of regions showed arousal-related activity that was distinct for different task conditions. Across these cortical regions, activity increased with slower HR. Arousal-related activity was distinct from task-evoked activity and it was robust to the inclusion of additional physiological nuisance regressors into the models. For the MSIT, such arousal-related activity occurred across visual and dorsal attention network regions. For the OBT, this activity occurred within fronto-parietal regions. For rest, arousal-related activity also occurred, but was confined to visual regions. The pulvinar nucleus of the thalamus showed arousal-related activity during all three task conditions. Widespread cortical activity, associated with increased parasympathetic arousal, may be propagated by thalamic circuits and contributes to improved attention. This activity is distinct from task-evoked activity, but affects cognitive performance and therefore should be incorporated into neurobiological models of cognition and clinical disorders., Competing Interests: Declaration of competing interest Dr. Malhotra has served as a consultant for Forum Pharmaceuticals and has served on a scientific advisory board for Genomind, Inc. Dr. Lencz has served as a consultant to Genomind, Inc. The other authors report no financial relationships with commercial interests., (Copyright © 2019. Published by Elsevier Inc.)

Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.

Aim: The Cannabis Experiences Questionnaire (CEQ) was developed to measure the subjective experiences of cannabis use both during and after intoxication. Despite the need to better understand the nature of the complex and significant relationship between cannabis use and early psychosis, this questionnaire has rarely been used in individuals with first-episode psychosis., Methods: We conducted a set of factor analyses using CEQ data from 194 first-episode psychosis patients who used cannabis in order to uncover the underlying factor structure of the questionnaire and thus the overarching types of psychological experiences during/after using cannabis in young people with psychotic disorders., Results: Our exploratory factor analysis identified 4 subscales, including: Distortions of Reality and Self-Perception (Factor 1), Euphoria Effects (Factor 2), Slowing and Amotivational Effects (Factor 3), and Anxiety and Paranoia Effects (Factor 4)., Conclusions: Elucidating the underlying factor structure of the CEQ in first-episode psychosis samples could help researchers move towards a deeper understanding of the types of experiences associated with cannabis intoxication among young adults with first-episode psychosis and could inform the development of programs designed to reduce use, improve the course of illness, and possibly delay or prevent the onset of psychotic symptoms in those at risk., (© 2017 John Wiley & Sons Australia, Ltd.)

Objective: Mood and anxiety disorders typically begin in adolescence or early adulthood, but those at the age of highest risk are among those least likely to access mental health services. However, they may be more likely than other demographic groups to seek help online. The goal of the present study was to investigate the online help- and information-seeking activity of young people newly diagnosed with mood and anxiety disorders in order to better understand how digital resources might serve this population., Method: Participants, aged 15 to 35, with a diagnosis of a mood or anxiety disorder were eligible if they had received their first mental health diagnosis within 24 months. Participants were interviewed with the Pathways to Care Questionnaire, which inquires about online activity prior to one's first interaction with mental healthcare providers., Results: Forty people participated (depression n = 30, bipolar disorder n = 5, generalized anxiety disorder n = 5); average age 21 years (SD=3.2), 60% female. Eighty-one percent reported seeking help and/or information about their symptoms online. The gap between symptom onset and in-person help seeking was 91.90 weeks (SD=133.7). Most participants (85%) reported they would be open to communicating with a mental health professional online., Conclusion: A majority of young people experiencing clinically-significant symptoms seek help online. However, the gap between symptom onset and treatment initiation remains unacceptably long. Better strategies are needed to translate young people's interest in online resources into meaningful care, whether through web-based services or facilitated pathways to traditional treatment., (Copyright © 2019. Published by Elsevier B.V.)

Omega-3 treatment studies for multi-episode schizophrenia or clinical high risk for conversion to psychosis states have had variable, and often negative, results. To examine adjunctive omega-3 treatment for recent onset psychosis, participants aged 15-40 years with recent onset schizophrenia-spectrum (n = 46) or bipolar (n = 4) disorders and current psychotic symptoms were treated for 16 weeks with risperidone and randomly-assigned omega-3 (EPA 740 mg and DHA 400 mg daily) or matching placebo. The primary outcome measure was the Brief Psychiatric Rating Scale (BPRS) total score. Mean lifetime antipsychotic exposure was 18.1 days. Length of time in treatment, risperidone dose and number of omega-3/placebo capsules taken did not differ between conditions. Longitudinal analysis of the total BPRS score revealed a trend level (p = 0.0826) treatment effect favoring omega-3 treatment. Lorazepam was an allowed concomitant medication. Among the subgroup (N = 23) who did not receive lorazepam, the treatment effect on BPRS total scores favoring omega-3 was significant (p = 0.0406) and factor scores analyses revealed a substantial decrease in depression-anxiety with omega-3 but no change with placebo (treatment-by-time interaction, p = 0.0184). Motor side effects did not differ between conditions. Analysis of Systematic Assessment for Treatment Emergent Events assessments revealed fewer adverse events overall with omega-3 compared with placebo with the largest differences between conditions (all favoring omega-3) on confusion, anxiety, depression, irritability, and tiredness/fatigue. These results suggest that omega-3 adjuvant treatment is a potential option for depression and anxiety symptoms of people with recent onset psychosis. Further research is needed to confirm this potential. Clinical trial registration: NCT01786239., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: The emphasis on reducing the duration of untreated psychosis (DUP) has highlighted complex barriers to accessing appropriate services. Internet and social media use by individuals with first-episode psychosis (FEP) was examined to explore how these platforms might be used to facilitate treatment initiation., Methods: Participants ages 15-35 were interviewed with the Pathways to Care for Psychosis Questionnaire, an 81-item instrument designed to explore online activity during symptom emergence., Results: Of 112 participants, 90% used the Internet and social media daily. The Internet was listed as the most used resource (62%) for information while symptoms were emerging. A minority (19%) shared concerns via social media, and 76% responded favorably to the possibility of receiving online mental health support., Conclusions: The Internet and social media were part of daily life for participants with FEP. Activity continued throughout the DUP, offering the prospect of earlier intervention. Participants expressed positive attitudes toward Internet-based outreach and engagement efforts.

Objective: Improving quality of life for people with first episode psychosis is an important aspect of recovery. Our objective was to review the associative factors on quality of life in first episode psychosis. A meta-analysis was conducted on the associations between quality of life, symptom severity, and duration of untreated psychosis., Method: Fifty-one articles were identified (published through 08/29/2016) that provided data on the relationship between quality of life and at least one other outcome measure of interest in first episode psychosis. Of those studies, 21 were included in a meta-analysis (n = 3992) of the relationship between quality of life, severity of psychosis, and duration of untreated psychosis., Results: Meta-analysis identified significant negative correlations between quality of life and severity of symptoms (total symptom scores: r = -0.32, p < 0.001) and quality of life and duration of untreated psychosis (r = -0.21, p < 0.001). Heinrich's quality of life scale emerged as being more sensitive to the presence of psychotic symptoms than other measures of quality of life., Conclusions: Associations were found between certain disease specific variables and quality of life in first episode psychosis, highlighting the relationship between symptom presentation and quality of life and the need for early intervention. Proper assessment of quality of life is important to promote improved quality of life in patients with first episode psychosis. Future research is needed to examine the interacting effects of symptom presentation, duration of untreated psychosis, and other variables, such as neurocognition, on quality of life., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: We identified, in subjects with first-episode psychosis, demographic and socioenvironmental predictors of three variables pertaining to premorbid marijuana use: age at initiation of marijuana use, trajectories of marijuana use in the five years prior to onset of psychosis, and the cumulative "dose" of marijuana intake in that same premorbid period., Methods: We enrolled 247 first-episode psychosis patients and collected data on lifetime marijuana/alcohol/tobacco use, age at onset of psychosis, diverse socioenvironmental variables, premorbid adjustment, past traumatic experiences, perceived neighborhood-level social disorder, and cannabis use experiences. Bivariate tests were used to examine associations between the three premorbid marijuana use variables and hypothesized predictors. Regression models determined which variables remained independently significantly associated., Results: Age at initiation of cigarette smoking was linked to earlier initiation, faster escalation, and higher cumulative dose of premorbid marijuana use. During childhood, poorer academic performance was predictive of an earlier age at initiation of marijuana use, while poorer sociability was related to more rapid escalation to daily use and a higher cumulative dose. As expected, experiencing euphoric effects was positively correlated with trajectories and cumulative dose, but having negative experiences was unrelated. Traumatic childhood/adolescent experiences were correlated with rapid escalation and amount of marijuana used, but not with age at initiation of marijuana use., Conclusion: These data expand the very limited literature on predictors of premorbid marijuana use in first-episode psychosis. Given its association with earlier age at onset of psychosis, and poorer outcomes among first-episode patients, prevention and treatment efforts should be further developed., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Technology provides an unparalleled opportunity to remove barriers to earlier identification and engagement in services for mental and addictive disorders by reaching people earlier in the course of illness and providing links to just-in-time, cost-effective interventions. Achieving this opportunity, however, requires stakeholders to challenge underlying assumptions about traditional pathways to mental health care. In this Open Forum, the authors highlight key issues discussed in the Technology for Early Awareness of Addiction and Mental Illness (TEAAM-I) meeting-held October 13-14, 2016, in New York City-that are related to three identified areas in which technology provides important and unique opportunities to advance early identification, increase service engagement, and decrease the duration of untreated mental and addictive disorders.

In October 2010, the Haitian Ministry of Public Health and Population (MSPP; Port au Prince, Haiti) reported a cholera epidemic caused by contamination of the Artibonite River by a United Nation Stabilization Mission camp. Interventional studies of the subsequent responses, including a descriptive Methods section and systematic approach, may be useful in facilitating comparisons and applying lessons learned to future outbreaks. The purpose of this study was to examine publicly available documents relating to the 2010 cholera outbreak to answer: (1) What information is publicly available on interventional studies conducted during the epidemic, and what was/were the impact(s)? and (2) Can the interventions be compared, and what lessons can be learned from their comparison? A PubMed (National Center for Biotechnology Information, National Institutes of Health; Bethesda, Maryland USA) search was conducted using the parameters "Haiti" and "cholera." Studies were categorized as "interventional research," "epidemiological research," or "other." A distinction was made between studies and narrative reports. The PubMed search yielded 171 papers, 59 (34.0%) of which were epidemiological and 12 (7.0%) were interventional studies. The remaining 100 papers (59.0%) comprised largely of narrative, anecdotal descriptions. An expanded examination of publications by the World Health Organization (WHO; Geneva, Switzerland), the Center for Research in the Epidemiology of Disasters (CRED; Brussels, Belgium), United States Agency for International Development (USAID; Washington, DC USA)-Development Experience Clearinghouse (DEC), and US National Library of Medicine's (NLM; Bethesda, Maryland USA) Disaster Literature databases yielded no additional interventional studies. The unstructured formats and differing levels of detail prohibited comparisons between interventions, even between those with a similar approach. Only two (17.0%) interventional studies included any impact data, although neither commented whether the intervention improved health or reduced incidence or mortality related to cholera. Agreed frameworks for guiding responses and subsequent reporting are needed to ensure reports contain sufficient detail to draw conclusions for the definition of best practices and for the design of future interventions. Miller J , Birnbaum ML . Characterization of interventional studies of the cholera epidemic in Haiti. Prehosp Disaster Med. 2018;33(2):176-181.

Objective: Identifying and engaging youth with early-stage psychotic disorders in order to facilitate timely treatment initiation remains a major public health challenge. Although advertisers routinely use the Internet to directly target consumers, limited efforts have focused on applying available technology to proactively encourage help-seeking in the mental health community. This study explores how one might take advantage of Google AdWords in order to reach prospective patients with early psychosis., Methods: A landing page was developed with the primary goal of encouraging help-seeking individuals in New York City to contact their local early psychosis intervention clinic. In order to provide the best opportunity to reach the intended audience, Google AdWords was utilized to link more than 2,000 selected search terms to strategically placed landing page advertisements. The campaign ran for 14 weeks between April 11 and July 18, 2016 and had a total budget of $1,427., Results: The ads appeared 191,313 times and were clicked on 4,350 times, at a per-click cost of $.33. Many users took additional help-seeking steps, including obtaining psychosis-specific information/education (44%), completing a psychosis self-screener (15%), and contacting the local early treatment program (1%)., Conclusions: Digital ads appear to be a reasonable and cost-effective method to reach individuals who are searching for behavioral health information online. More research is needed to better understand the many complex steps between online search inquiries and making first clinical contact.