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. 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.

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 â€‹= â€‹20) underwent multi-band echo-planar imaging (TR â€‹= â€‹0.72 â€‹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.

Characterizing Awareness of Schizophrenia Among Facebook Users by Leveraging Facebook Advertisement Estimates.

BACKGROUND: Schizophrenia is a rare but devastating condition, affecting about 1% of the world's population and resulting in about 2% of the US health care expenditure. Major impediments to appropriate and timely care include misconceptions, high levels of stigma, and lack of public awareness. Facebook offers novel opportunities to understand public awareness and information access related to schizophrenia, and thus can complement survey-based approaches to assessing awareness that are limited in scale, robustness, and temporal and demographic granularity. OBJECTIVE: The aims of this study were to (1) construct an index that measured the awareness of different demographic groups around schizophrenia-related information on Facebook; (2) study how this index differed across demographic groups and how it correlated with complementary Web-based (Google Trends) and non-Web-based variables about population well-being (mental health indicators and infrastructure), and (3) examine the relationship of Facebook derived schizophrenia index with other types of online activity as well as offline health and mental health outcomes and indicators. METHODS: Data from Facebook's advertising platform was programmatically collected to compute the proportion of users in a target demographic group with an interest related to schizophrenia. On consultation with a clinical expert, several topics were combined to obtain a single index measuring schizophrenia awareness. This index was then analyzed for differences across US states, gender, age, ethnic affinity, and education level. A statistical approach was developed to model a group's awareness index based on the group's characteristics. RESULTS: Overall, 1.03% of Facebook users in the United States have a schizophrenia-related interest. The schizophrenia awareness index (SAI) is higher for females than for males (1.06 vs 0.97, P<.001), and it is highest for the people who are aged 25-44 years (1.35 vs 1.03 for all ages, P<.001). The awareness index drops for higher education levels (0.68 for MA or PhD vs 1.92 for no high school degree, P<.001), and Hispanics have the highest level of interest (1.57 vs 1.03 for all ethnic affinities, P<.001). A regression model fit to predict a group's interest level achieves an adjusted R2=0.55. We also observe a positive association between our SAI and mental health services (or institutions) per 100,000 residents in a US state (Pearson r=.238, P<.001), but a negative association with the state-level human development index (HDI) in United States (Pearson r=-.145, P<.001) and state-level volume of mental health issues in United States (Pearson r=-.145, P<.001). CONCLUSIONS: Facebook's advertising platform can be used to construct a plausible index of population-scale schizophrenia awareness. However, only estimates of awareness can be obtained, and the index provides no information on the quality of the information users receive online.

A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals.

BACKGROUND: Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures. OBJECTIVE: This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals. METHODS: Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users. RESULTS: Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier's precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively. CONCLUSIONS: These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses' biggest challenges by using digital technology.

A Systematic Review and Taxonomy of Published Quality Criteria Related to the Evaluation of User-Facing eHealth Programs.

The purpose of this review was to identify and classify key criteria concepts related to the evaluation of user-facing eHealth programs. In line with the PRISMA statement methodology, computer searches of relevant databases were conducted for studies published between January 1, 2000 and March 1, 2016 that contained explicit quality criteria related to mHealth and eHealth products. Reference lists of included articles, review articles, and grey literature (e.g., books, websites) were searched for additional sources. A team of nine experts led by the first author was gathered to support the classification of these criteria. Identified criteria were extracted, grouped and organized using an inductive thematic analysis. Eighty-four sources - emanating from 26 different courtiers - were included in this review. The team extracted 454 criteria that were grouped into 11 quality domains, 58 criteria concepts and 134 concepts' sub-groups. Quality domains were: Usability, Visual Design, User Engagement, Content, Behavior Change/Persuasive Design, Influence of Social Presence, Therapeutic Alliance, Classification, Credibility/Accountability, and Privacy/Security. Findings suggest that authors around the globe agree on key criteria concepts when evaluating user-facing eHealth products. The high proportion of new published criteria in the second half of this review time-frame (2008-2016), and more specifically, the high proportion of criteria relating to persuasive design, therapeutic alliance and privacy/security within this time-frame, points to the advancements made in recent years within this field.

Suicidality and hospitalization as cause and outcome of pediatric psychiatric emergency room visits.

The aim of this study was to identify predictors of suicidality in youth presenting to a pediatric psychiatric emergency room service (PPERS). To this end, we conducted a retrospective cohort study of youth aged <18 years consecutively assessed by a PPERS 01.01.2002-12.31.2002, using a 12-page semi-structured institutional evaluation form and the Columbia Classification Algorithm for Suicide Assessment. Multivariate regression analyses were conducted to identify correlates of suicidal thoughts and attempts/preparation and their relationship to outpatient/inpatient disposition. Of 1,062 youth, 265 (25.0%) presented with suicidal ideation (16.2%) or attempt/preparation (8.8%). Suicidal ideation was associated with female sex, depression, adjustment disorder, absent referral by family/friend/self, school referral, precipitant of peer conflict, and no antipsychotic treatment (p < 0.0001). Suicidal attempt/preparation was associated with female sex, depression, lower GAF score, past suicide attempt, precipitant of peer conflict, and no stimulant treatment (p < 0.0001). Compared to suicidal attempt/preparation, suicidal ideation was associated with school referral, and higher GAF score (p < 0.0001). Of the 265 patients with suicidality, 58.5% were discharged home (ideation = 72.1% vs. attempt/preparation = 33.7%, p < 0.0001). In patients with suicidal ideation, outpatient disposition was associated with higher GAF score, school referral, and adjustment disorder (p < 0.0001). In patients with suicidal attempt/preparation, outpatient disposition was associated with higher GAF score, lower acuity rating, and school referral (p < 0.0001). Suicidality is common among PPERS evaluations. Higher GAF score and school referral distinguished suicidal ideation from suicidal attempt/preparation and was associated with outpatient disposition in both presentations. Increased education of referral sources and establishment of different non-PPERS evaluation systems may improve identification of non-emergent suicidal presentations and encourage more appropriate outpatient referrals.

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. 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.

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. 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.

Pharmacoepidemiology of antipsychotic use in youth with ADHD: trends and clinical implications.

Although concern has been raised about antipsychotic prescribing to youth with attention-deficit/hyperactivity disorder (ADHD), the available database is limited to individual studies. Therefore, in order to provide a synthesis of prevalence and time trends, we conducted a systematic review and pooled analysis of pharmaco-epidemiologic data on antipsychotic use in ADHD youth. Of 1806 hits, 21 studies (N) were retained that reported analyzable data for three separate populations: 1) antipsychotic-treated youth (N = 15, n = 341,586); 2) ADHD youth (N = 9, n = 6,192,368), and 3) general population youth (N = 5, n = 14,284,916). Altogether, 30.5 ± 18.5% of antipsychotic-treated youth had ADHD. In longitudinal studies, this percentage increased over time (1998-2007) from 21.7 ± 7.1% to 27.7 ± 7.7%, ratio = 1.3 ± 0.4. Furthermore, 11.5 ± 17.5% of ADHD youth received antipsychotics. In longitudinal studies, this percentage also increased (1998-2006) from 5.5 ± 2.6% to 11.4 ± 6.7%, ratio = 2.1 ± 0.6. Finally, 0.12 ± 0.07% of youth in the general population were diagnosed with ADHD and received antipsychotics. Again, in longitudinal studies, this percentage increased over time (1993-2007): 0.13 ± 0.09% to 0.44 ± 0.49%, ratio = 3.1 ± 2.2. Taken together, these data indicate that antipsychotics are used by a clinically relevant and increasing number of youth with ADHD. Reasons for and risk/benefit ratios of this practice with little evidence base require further investigation.

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. 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.

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. 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.

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. 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 F1-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 F1-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.

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. 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.

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. 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.

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. 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.

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. 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.

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.

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 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.

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. 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.

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). 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.