Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study.

BACKGROUND: Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. METHODS: This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). RESULTS: Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LIMITATIONS: Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. CONCLUSIONS: Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

Race and Socioeconomic Status as Predictors of Willingness to Use Digital Mental Health Interventions or One-On-One Psychotherapy: National Survey Study.

BACKGROUND: There is an ongoing debate about whether digital mental health interventions (DMHIs) can reduce racial and socioeconomic inequities in access to mental health care. A key factor in this debate involves the extent to which racial and ethnic minoritized individuals and socioeconomically disadvantaged individuals are willing to use, and pay for, DMHIs. OBJECTIVE: This study examined racial and ethnic as well as socioeconomic differences in participants' willingness to pay for DMHIs versus one-on-one therapy (1:1 therapy). METHODS: We conducted a national survey of people in the United States (N=423; women: n=204; mean age 45.15, SD 16.19 years; non-Hispanic White: n=293) through Prolific. After reading descriptions of DMHIs and 1:1 therapy, participants rated their willingness to use each treatment (1) for free, (2) for a small fee, (3) as a maximum dollar amount, and (4) as a percentage of their total monthly income. At the end of the study, there was a decision task to potentially receive more information about DMHIs and 1:1 therapy. RESULTS: Race and ethnicity was associated with willingness to pay more of one's income, as a percent or in dollar amounts, and was also associated with information-seeking for DMHIs in the behavioral task. For most outcomes, race and ethnicity was not associated with willingness to try 1:1 therapy. Greater educational attainment was associated to willingness to try DMHIs for free, the decision to learn more about DMHIs, and willingness to pay for 1:1 therapy. Income was inconsistently associated to willingness to try DMHIs or 1:1 therapy. CONCLUSIONS: If they are available for free or at very low costs, DMHIs may reduce inequities by expanding access to mental health care for racial and ethnic minoritized individuals and economically disadvantaged groups.

Race, Ethnicity, and Other Cultural Background Factors in Trials of Internet-Based Cognitive Behavioral Therapy for Depression: Systematic Review.

BACKGROUND: There is a growing interest in developing scalable interventions, including internet-based cognitive behavioral therapy (iCBT), to meet the increasing demand for mental health services. Given the growth in diversity worldwide, it is essential that the clinical trials of iCBT for depression include diverse samples or, at least, report information on the race, ethnicity, or other background indicators of their samples. Unfortunately, the field lacks data on how well diversity is currently reported and represented in the iCBT literature. OBJECTIVE: Thus, the main objective of this systematic review was to examine the overall reporting of racial and ethnic identities in published clinical trials of iCBT for depression. We also aimed to review the representation of specific racial and ethnic minoritized groups and the inclusion of alternative background indicators such as migration status or country of residence. METHODS: Studies were included if they were randomized controlled trials in which iCBT was compared to a waiting list, care-as-usual, active control, or another iCBT. The included papers also had to have a focus on acute treatment (eg, 4 weeks to 6 months) of depression, be delivered via the internet on a website or a smartphone app and use guided or unguided self-help. Studies were initially identified from the METAPSY database (n=59) and then extended to include papers up to 2022, with papers retrieved from Embase, PubMed, PsycINFO, and Cochrane (n=3). Risk of bias assessment suggested that reported studies had at least some risk of bias due to use of self-report outcome measures. RESULTS: A total of 62 iCBT randomized controlled trials representing 17,210 participants are summarized in this study. Out of those 62 papers, only 17 (27%) of the trials reported race, and only 12 (19%) reported ethnicity. Reporting outside of the United States was very poor, with the United States accounting for 15 (88%) out of 17 of studies that reported race and 9 (75%) out of 12 for ethnicity. Out of 3,623 participants whose race was reported in the systematic review, the racial category reported the most was White (n=2716, 74.9%), followed by Asian (n=209, 5.8%) and Black (n=274, 7.6%). Furthermore, only 25 (54%) out of the 46 papers conducted outside of the United States reported other background demographics. CONCLUSIONS: It is important to note that the underreporting observed in this study does not necessarily indicate an underrepresentation in the actual study population. However, these findings highlight the poor reporting of race and ethnicity in iCBT trials for depression found in the literature. This lack of diversity reporting may have significant implications for the scalability of these interventions.

Negative affect variability differs between anxiety and depression on social media.

OBJECTIVE: Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications. METHOD: Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content. RESULTS: Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. LIMITATIONS: Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect. CONCLUSIONS: Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.

Availability of Internet-Based Cognitive-Behavioral Therapies for Depression: A Systematic Review.

We examined the availability and components of internet-based cognitive-behavioral therapies (iCBTs) for depression tested in randomized-controlled trials (RCTs). The objectives of this literature review were to determine the extent to which research-validated iCBTs were available to the public, as well as to determine their therapeutic content. A literature review of RCTs for iCBTs was conducted on July 30, 2021. For each iCBT, interventions were rated by content and compared to commercially available smartphone apps. Our search yielded 80 studies using 41 unique iCBTs. Of these, only 6 (15%) were completely available to the public, more than half were not publicly available (46%), and the remaining 39% were available to the public with some restrictions (e.g., those based on the user's geographical location). When comparing iCBTs evaluated in RCTs to commercially available smartphone apps, we found that iCBTs were more likely to contain psychoeducation, cognitive restructuring, behavioral activation, problem solving, and interpersonal communication components. iCBTs from RCTs contain evidence-based content but few are available to the public. Extending beyond efficacy, attention should be paid to the dissemination of iCBTs.

Commentary: Holy grails, personalized medicine, and the public health burden of psychopathology - a reflection on Ahuvia et al. (2023).

Clinical psychology and psychiatry have many 'holy grails' or research findings that are widely sought after but remain elusive. The use of machine learning (ML) models for treatment selection is one of these holy grails. Ahuvia et al. (Journal of Child Psychology and Psychiatry, 2023) recently analyzed a large trial (n = 996) of two distinct single-session interventions (SSIs) for internalizing distress and found little evidence that an ML model could predict differential treatment response. I discuss potential avenues for advancing SSI research. One avenue is the dissemination and implementation of SSIs, including how they interact with other treatments in routine care. Quantifying and critically questioning the promises of holy grails like ML models is sorely needed. Using simulation modeling to evaluate the relative merits of using ML models for treatment selection or using SSIs versus other treatment strategies may be another path forward.

Mental health misinformation on social media: Review and future directions.

Social media use for health information is extremely common in the United States. Unfortunately, this use may expose users to misinformation. The prevalence and harms of misinformation are well documented in many health domains (e.g., infectious diseases). However, research on mental health misinformation is limited. Our review suggests that mental health misinformation is common, although its prevalence varies across disorders and treatment types. Individual differences in susceptibility to misinformation have been documented for health misinformation generally but less so for mental health specifically. We discuss conceptual issues in defining mental health misinformation versus other classifications such as overgeneralizations from personal experience. Although there is clear evidence for false and actively misleading content, future research should also explore the role of negative healthcare experiences and health disparities on mental health misinformation on social media.

Is a Criterion A trauma necessary to elicit posttraumatic stress symptoms?

OBJECTIVE: The posttraumatic stress disorder (PTSD) diagnosis has undergone substantial revision since its first appearance in the DSM-III. Much of the controversy surrounds the definition of trauma, or Criterion A. Our study sought to evaluate the DSM-5-TR's Criterion A and severity of PTSD symptoms in college students. METHOD: Participants were 1500 college students who completed an online questionnaire about mental health symptoms. Responses to the Criterion A assessment were double coded by researchers to determine if the DSM-5-TR's Criterion A was met. Interpersonal agreement between raters was high (kappa = .81). Participants were compared across groups based on their PTSD Criterion A status: (1) DSM-Congruent, (2) DSM-Incongruent, (3) DSM-Ambiguous, and (4) Denied Trauma, using analysis of variance and multiple regression. RESULTS: Participants who reported a trauma that was coded as Criterion A by researchers had the highest levels of PTSD symptoms, even after controlling for perceived stress, depression, anxiety, and gender (p < .001). Comparing across groups, the DSM-Congruent Criterion A group had significantly higher overall PTSS than those in the DSM-Incongruent Criterion A group and also significantly higher hyperarousal symptoms. However, the DSM-Congruent Criterion A group did not differ from the DSM-Ambiguous trauma group on any PTSD symptom cluster. CONCLUSIONS: The lack of significant differences in scores between individuals with DSM- Congruent, DSM-Incongruent, and DSM-Ambiguous traumas provides evidence about the subjective nature of trauma and how college-age individuals interpret their symptoms of PTSD. Clinical implications are discussed.

Methods for quantifying the heterogeneity of psychopathology.

OBJECTIVES: Specifiers for a major depressive disorder (MDE) are supposed to reduce diagnostic heterogeneity. However, recent literature challenges the idea that the atypical and melancholic specifiers identify more homogenous or coherent subgroups. We introduce the usage of distance metrics to characterize symptom heterogeneity. We attempt to replicate prior findings and explore whether symptom heterogeneity is reduced using specifier subgroups. METHODS: We used data derived from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC Wave I; N = 5,749) and the Sequenced Treatment Alternatives to Relieve Depression study (STAR*D; N = 2,498). We computed Hamming and Manhattan distances from study participants' unique symptom profiles. Distances were standardized from 0-1 and compared by their within- and between-group similarities to their non-specifier counterparts for the melancholic and atypical specifiers. RESULTS: There was no evidence of statistically significant differences in heterogeneity for specifier (i.e., melancholic or atypical) vs. non-specifier designations (i.e., non-melancholic vs. non-atypical). CONCLUSION: Replicating prior work, melancholic and atypical depression specifiers appear to have limited utility in reducing heterogeneity. The current study does not support the claim that specifiers create more coherent subgroups as operationalized by similarity in the number of symptoms and their severity. Distance metrics are useful for quantifying symptom heterogeneity.

"I would not want the mechanic to direct me to an engine repair manual": a qualitative analysis of provider perspectives on low-intensity treatments for patients on waiting lists.

BACKGROUND: Low-intensity treatments (LITs), such as bibliotherapy or online self-help, have the potential to reach more individuals than traditional face-to-face care by circumventing many of the common barriers to mental health treatment. Despite substantial research evidence supporting their usability and efficacy across several clinical presentations, prior work suggests that mental health providers rarely recommend LITs for patients waiting for treatment. METHODS: The present study analyzed provider open responses to a prompt asking about perceived barriers, thoughts, and comments related to additional treatment resources for patients on treatment waiting lists. We surveyed 141 practicing mental health providers, 65 of whom responded to an open text box with additional thoughts on using LITs for patients on treatment waiting lists. Responses were qualitatively coded using a thematic coding process. RESULTS: Qualitative outcomes yielded 11 codes: patient appropriateness, research evidence, feasibility, patient barriers, liability, patient personal contact, additional resources, positive attitudes, trust in programs, systemic problems, and downplaying distress. CONCLUSIONS: Results suggest providers are predominantly concerned about the potential of suggesting a LIT that would be ultimately inappropriate for their patient due to a lack of assessment of the patient's needs. Furthermore, providers noted ambiguity around the legal and ethical liability of recommending a LIT to someone who may not yet be a patient. Guidelines and standards for recommending LITs to patients on treatment waiting lists may help address ambiguity regarding their use in routine care.

Efficacy of an Unguided, Digital Single-Session Intervention for Internalizing Symptoms in Web-Based Workers: Randomized Controlled Trial.

BACKGROUND: The Common Elements Toolbox (COMET) is an unguided digital single-session intervention (SSI) based on principles of cognitive behavioral therapy and positive psychology. Although unguided digital SSIs have shown promise in the treatment of youth psychopathology, the data are more mixed regarding their efficacy in adults. OBJECTIVE: This study aimed to investigate the efficacy of COMET-SSI versus a waiting list control in depression and other transdiagnostic mental health outcomes for Prolific participants with a history of psychopathology. METHODS: We conducted an investigator-blinded, preregistered randomized controlled trial comparing COMET-SSI (n=409) with an 8-week waiting list control (n=419). Participants were recruited from the web-based workspace Prolific and assessed for depression, anxiety, work and social functioning, psychological well-being, and emotion regulation at baseline and at 2, 4, and 8 weeks after the intervention. The main outcomes were short-term (2 weeks) and long-term (8 weeks) changes in depression and anxiety. The secondary outcomes were the 8-week changes in work and social functioning, well-being, and emotion regulation. Analyses were conducted according to the intent-to-treat principle with imputation, without imputation, and using a per-protocol sample. In addition, we conducted sensitivity analyses to identify inattentive responders. RESULTS: The sample comprised 61.9% (513/828) of women, with a mean age of 35.75 (SD 11.93) years. Most participants (732/828, 88.3%) met the criteria for screening for depression or anxiety using at least one validated screening scale. A review of the text data suggested that adherence to the COMET-SSI was near perfect, there were very few inattentive respondents, and satisfaction with the intervention was high. However, despite being powered to detect small effects, there were negligible differences between the conditions in the various outcomes at the various time points, even when focusing on subsets of individuals with more severe symptoms. CONCLUSIONS: Our results do not support the use of the COMET-SSI in adult Prolific participants. Future work should explore alternate ways of intervening with paid web-based participants, including matching individuals to SSIs they may be most responsive to. TRIAL REGISTRATION: ClinicalTrials.gov NCT05379881, https://clinicaltrials.gov/ct2/show/NCT05379881.

Identifying active ingredients in cognitive-behavioral therapies: What if we didn't?

Identifying active ingredients of psychological interventions is a major goal of psychotherapy researchers that is often justified by the promise that it will lead to improved patient outcomes. Much of this "active ingredients" research is conducted within randomized controlled trials (RCTs) with patient populations, putting it in Phase T2 of the clinical-translational spectrum. I argue that RCTs in patient populations are very "messy laboratories" in which to conduct active ingredient work and that T0 and T1 research provide more controlled contexts. However, I call attention to the long road from identifying active ingredients of CBTs, whether in T0, T1, or T2 research, to improving outcomes. Dissemination and implementation research (T3 and T4 approaches) may be conceptually closer to improving outcomes. Given how common and disabling mental health symptoms are, I argue that if researchers want to improve patient outcomes, these research programs must receive more attention including work on the uptake of psychological interventions as well as work on optimal ordering of existing interventions.

Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning.

BACKGROUND: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. OBJECTIVE: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. METHODS: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. RESULTS: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. CONCLUSIONS: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.

"I Haven't Been Diagnosed, but I Should Be"-Insight Into Self-diagnoses of Common Mental Health Disorders: Cross-sectional Study.

BACKGROUND: In recent years, social media has become a rich source of mental health data. However, there is a lack of web-based research on the accuracy and validity of self-reported diagnostic information available on the web. OBJECTIVE: An analysis of the degree of correspondence between self-reported diagnoses and clinical indicators will afford researchers and clinicians higher levels of trust in social media analyses. We hypothesized that self-reported diagnoses would correspond to validated disorder-specific severity questionnaires across 2 large web-based samples. METHODS: The participants of study 1 were 1123 adults from a national Qualtrics panel (mean age 34.65, SD 12.56 years; n=635, 56.65% female participants,). The participants of study 2 were 2237 college students from a large university in the Midwest (mean age 19.08, SD 2.75 years; n=1761, 75.35% female participants). All participants completed a web-based survey on their mental health, social media use, and demographic information. Additionally, the participants reported whether they had ever been diagnosed with a series of disorders, with the option of selecting "Yes"; "No, but I should be"; "I don't know"; or "No" for each condition. We conducted a series of ANOVA tests to determine whether there were differences among the 4 diagnostic groups and used post hoc Tukey tests to examine the nature of the differences. RESULTS: In study 1, for self-reported mania (F3,1097=2.75; P=.04), somatic symptom disorder (F3,1060=26.75; P<.001), and alcohol use disorder (F3,1097=77.73; P<.001), the pattern of mean differences did not suggest that the individuals were accurate in their self-diagnoses. In study 2, for all disorders but bipolar disorder (F3,659=1.43; P=.23), ANOVA results were consistent with our expectations. Across both studies and for most conditions assessed, the individuals who said that they had been diagnosed with a disorder had the highest severity scores on self-report questionnaires, but this was closely followed by individuals who had not been diagnosed but believed that they should be diagnosed. This was especially true for depression, generalized anxiety, and insomnia. For mania and bipolar disorder, the questionnaire scores did not differentiate individuals who had been diagnosed from those who had not. CONCLUSIONS: In general, if an individual believes that they should be diagnosed with an internalizing disorder, they are experiencing a degree of psychopathology similar to those who have already been diagnosed. Self-reported diagnoses correspond well with symptom severity on a continuum and can be trusted as clinical indicators, especially in common internalizing disorders such as depression and generalized anxiety disorder. Researchers can put more faith into patient self-reports, including those in web-based experiments such as social media posts, when individuals report diagnoses of depression and anxiety disorders. However, replication and further study are recommended.

Acceptability and Outcomes of Transdiagnostic Guided Self-help Bibliotherapy for Internalizing Disorder Symptoms in Adults: A Fully Remote Nationwide Open Trial.

Introduction: Doing What Matters in Times of Stress (DWM) is a five-module transdiagnostic guided self-help (GSH) intervention developed by the World Health Organization, originally in a group-based format. In a sample of individuals recruited from across the United States, we conducted an open trial to study the feasibility and acceptability of an adaptation of DWM in which guidance was provided individually and remotely via phone and videoconferencing. Methods: We assessed internalizing symptoms, psychological well-being, work and social functioning, usability of the intervention, and emotion regulation over the course of 6 weeks. Results: A total of 263 individuals completed our screening. Of those, 75.29% (n = 198) qualified for the intervention. We reached most participants who qualified (71.21%, n = 141) via phone to schedule a GSH session. Most of those scheduled attended a study session (84.4%, n = 119), and most of those who attended a session completed more than half the treatment (84.03%, n = 100). Retention rates were comparable to meta-analytic estimates of dropout rates in GSH. Participants showed improvement on internalizing symptoms, psychological well-being, work and social functioning, usability of the intervention, and emotion regulation. Conclusion: DWM is a freely available, seemingly efficacious transdiagnostic intervention for internalizing disorder symptoms. Supplementary Information: The online version contains supplementary material available at 10.1007/s10608-022-10338-5.

Outcomes of student trainee-delivered cognitive-behavioral therapy (CBT) on internalizing symptoms, CBT skills, and life satisfaction.

Increased quality of life (QoL) is rated by patients as a primary factor in determining recovery from psychopathology. Cognitive-behavioral therapies (CBTs) are the most well-researched psychotherapies for internalizing disorders and appear effective at reducing symptoms even when delivered by trainees. Existing research suggests that the effects of CBTs on QoL are more modest than their effects on symptoms. However, little is known about the effects of trainee-delivered CBT on life satisfaction, a subjective measure of QoL. We analyzed data from 93 clients treated by students (n=23) in a graduate-level training clinic using an intent-to-treat approach, completers case analyses, and random forest imputation. Across methods of handling missing data, improvements in anxiety, depression, and CBT skills were more marked than improvements in QoL. Exploratory analyses suggested baseline life satisfaction was the strongest predictor of end-of-treatment life satisfaction. Future research should explore alternatives to "standard" CBT for clients with low life satisfaction.

Sociodemographics and Transdiagnostic Mental Health Symptoms in SOCIAL (Studies of Online Cohorts for Internalizing Symptoms and Language) I and II: Cross-sectional Survey and Botometer Analysis.

BACKGROUND: Internalizing, externalizing, and somatoform disorders are the most common and disabling forms of psychopathology. Our understanding of these clinical problems is limited by a reliance on self-report along with research using small samples. Social media has emerged as an exciting channel for collecting a large sample of longitudinal data from individuals to study psychopathology. OBJECTIVE: This study reported the results of 2 large ongoing studies in which we collected data from Twitter and self-reported clinical screening scales, the Studies of Online Cohorts for Internalizing Symptoms and Language (SOCIAL) I and II. METHODS: The participants were a sample of Twitter-using adults (SOCIAL I: N=1123) targeted to be nationally representative in terms of age, sex assigned at birth, race, and ethnicity, as well as a sample of college students in the Midwest (SOCIAL II: N=1988), of which 61.78% (1228/1988) were Twitter users. For all participants who were Twitter users, we asked for access to their Twitter handle, which we analyzed using Botometer, which rates the likelihood of an account belonging to a bot. We divided participants into 4 groups: Twitter users who did not give us their handle or gave us invalid handles (invalid), those who denied being Twitter users (no Twitter, only available for SOCIAL II), Twitter users who gave their handles but whose accounts had high bot scores (bot-like), and Twitter users who provided their handles and had low bot scores (valid). We explored whether there were significant differences among these groups in terms of their sociodemographic features, clinical symptoms, and aspects of social media use (ie, platforms used and time). RESULTS: In SOCIAL I, most individuals were classified as valid (580/1123, 51.65%), and a few were deemed bot-like (190/1123, 16.91%). A total of 31.43% (353/1123) gave no handle or gave an invalid handle (eg, entered "N/A"). In SOCIAL II, many individuals were not Twitter users (760/1988, 38.23%). Of the Twitter users in SOCIAL II (1228/1988, 61.78%), most were classified as either invalid (515/1228, 41.94%) or valid (484/1228, 39.41%), with a smaller fraction deemed bot-like (229/1228, 18.65%). Participants reported high rates of mental health diagnoses as well as high levels of symptoms, especially in SOCIAL II. In general, the differences between individuals who provided or did not provide their social media handles were small and not statistically significant. CONCLUSIONS: Triangulating passively acquired social media data and self-reported questionnaires offers new possibilities for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is likely not a source of sample bias in subsequent social media analytics.