Exploring online and offline social experiences and interaction patterns of young adults with psychosis with the social media and internet social engagement questionnaire: Analyses and future directions.

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.

Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.

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.

How are Conversations via an On-Demand Peer-To-Peer Emotional Well-Being App Associated with Emotional Improvement?

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.

Examining the Effectiveness of a Digital Media Campaign at Reducing the Duration of Untreated Psychosis in New York State: Results From a Stepped-wedge Randomized Controlled Trial.

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.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis.

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.

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.

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.

Digital Technology in Psychiatry: Survey Study of Clinicians.

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.

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis.

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).

Digital Strategies to Accelerate Help-Seeking in Youth With Psychiatric Concerns in New York State.

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 Reintegration Journey Following A Psychiatric Hospitalization: Examining the Role of Social Technologies.

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.

A Serious Game for Young People With First Episode Psychosis (OnTrack>The Game): Qualitative Findings of a Randomized Controlled Trial.

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.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development.

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.

Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions.

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.

Clinician Perspectives on Using Computational Mental Health Insights From Patients' Social Media Activities: Design and Qualitative Evaluation of a Prototype.

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.

Contributions of Parasympathetic Arousal-Related Activity to Cognitive Performance in Patients With First-Episode Psychosis and Control Subjects.

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.

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders.

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.

Developing a theoretical framework for persistent cannabis use among young adults with first episode psychosis.

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.

A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations.

For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of such as removal of symptoms, and 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.

Psychoeducation for Inpatients With First-Episode Psychosis: Results From a Survey of Psychiatry Trainees in New York City.

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.

Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook.

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.

Identifying emerging mental illness utilizing search engine activity: A feasibility study.

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.

Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study.

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.

Designing a Clinician-Facing Tool for Using Insights From Patients' Social Media Activity: Iterative Co-Design Approach.

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.

Parasympathetic arousal-related cortical activity is associated with attention during cognitive task performance.

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.