Lorenzo Lorenzo-Luaces

Diagnostic heterogeneity is a longstanding challenge for the classification of mental illness because people who receive the same diagnosis can differ markedly in their symptom presentations as well as in the mechanisms underlying those symptoms. High heterogeneity undermines both the clinical utility of diagnostic labels and what we can learn from research that treats disorders as coherent constructs. In this study, we examined heterogeneity across six internalizing disorders (specific phobia, social anxiety disorder, agoraphobia, generalized anxiety disorder [GAD], major depressive disorder [MDD], and posttraumatic stress disorder [PTSD]) using three complementary approaches to quantifying heterogeneity: 1) descriptive heterogeneity (how many symptom profiles can theoretically satisfy the diagnostic criteria, and how many are observed in practice), 2) person-focused heterogeneity (whether diagnostic labels capture people with similar symptom presentations), and 3) construct-focused heterogeneity (whether diagnostic labels group symptoms with similar patterns of association with external validators). Analyses drew on subsamples from a large convenience sample with elevated psychopathology, predominantly from Australia and the USA (n = 14,762, mean [SD] age = 38.0 [18.0]; 55% women). Results showed that specific phobia was consistently the least heterogeneous; social anxiety disorder, agoraphobia, and GAD had mixed results; and MDD and PTSD were highly heterogeneous across all metrics. Notably, empirically derived phenotypes nested within each disorder substantively reduced heterogeneity relative to the DSM diagnoses. The study provides both a systematic multi-method approach to characterising different types of diagnostic heterogeneity, and a practical path toward more precise and informative constructs that can be implemented by rescoring existing data.

Some digital single-session interventions (SSI) for depression appear effective, at least in youths, but not everyone benefits. In the present study, we use machine-learning methods to develop a treatment-matching algorithm for a digital SSI, the Common Elements Toolbox (COMET), versus a waitlist control. Eight hundred twenty-eight adults with a current or past mental-health problem were randomly assigned to COMET or a waitlist control. Elastic-net-regularization models with 10-fold cross-validation were used to develop a Personalized Advantage Index (PAI) indicating the relative benefit of receiving COMET over the waitlist in 2-week posttreatment depressive symptoms. In the 20% held-out test data, PAI did not interact with treatment to predict depression severity after treatment (β = 0.880, SE = 1.21, t = −0.72, p = .47), indicating that our treatment-matching algorithm was not able to provide statistically significant recommendations. Even in a large sample, personalized treatment recommendations for digital SSIs are difficult to develop.

Processes of change in cognitive behavioral therapies (CBTs) remain frequently debated, perhaps due to methodological difficulties in conducting rigorous process research. We investigated two contrasting emotion regulation strategies, cognitive reappraisal and expressive suppression, as processes of change in CBT disaggregating within- from between-person changes. We examined whether week-to-week changes in cognitive reappraisal and expressive suppression (Emotion Regulation Questionnaire) predicted subsequent changes in distress (Kessler-6 Psychological Distress Scale) and well-being (World Health Organization-5 Well-Being Index) pooling two trials of a guided self-help CBT based on acceptance and commitment therapy. We used a random-intercept cross-lagged panel model to disaggregate between- versus within-person changes. Participants (N = 274) were 82% female, 80% non-Hispanic White, and mean age 37 years (SD = 13). At the within-person level, week-to-week changes in reappraisal were consistently associated with subsequent changes in distress but less consistently with well-being. Changes in expressive suppression were less consistently associated with subsequent changes in distress or well-being. The associations between reappraisal and distress were robust to different model specifications, including detrending. Cognitive reappraisal may be involved in the process of change in CBTs, including the so-called "third-wave" CBTs, which have traditionally de-emphasized its importance. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background: Social media use among older adults continues to grow. Many people use social media to establish meaningful social ties and discuss their mental health. Depression in midlife and older adults is a critical public health concern, yet scalable, sensitive methods for early detection remain limited. Natural language processing offers new opportunities to examine sentiment and mental health through online language in typically understudied populations. Objective: This study examined whether sentiment and cognitive features of naturalistic social media language are associated with depression diagnosis and symptom severity among midlife and older adults. Methods: We constructed cohorts of adults aged 50 years and older from Reddit (n=688) and Bluesky (n=210). Participants who had ever had depression and never been diagnosed were recruited via a self-report survey on Prolific that collected depression history, age of diagnosis, current symptoms, and social media handles. Public posts were preprocessed and analyzed using a validated rule-based sentiment analysis (Valence Aware Dictionary and Sentiment Reasoner). Sentiment measures were aggregated at the user level and compared by depression status within and across platforms. Results: On Bluesky, users with a history of depression exhibited lower Valence Aware Dictionary and Sentiment Reasoner compound sentiment compared to users who had never been diagnosed (ρ=−0.16; P <.001). On Reddit, current depression severity showed a small association with lower compound sentiment, and although the bootstrap CI excluded zero, the association did not remain statistically significant after correction for multiple comparisons ( P >.05). These findings indicate a platform-specific relationship between depression status and online sentiment. Conclusions: Negative sentiment expressed on Bluesky was associated with depression history (ever diagnosed vs never diagnosed) among midlife and older adults. On Reddit, current depression severity showed a small association with negative sentiment that did not remain statistically significant after correction for multiple comparisons. Our findings suggest that sentiment-based language features may capture modest differences in online expression related to depression. Future work with larger samples and longitudinal symptom assessment is needed to determine whether sentiment reliably tracks with depression-related symptoms over time.

Objective: Processes of change in cognitive behavioral therapies (CBTs) remain frequently debated, perhaps due to methodological difficulties in conducting good process research. We investigated two contrasting emotion regulation strategies, cognitive reappraisal and expressive suppression, as processes of change in CBT disaggregating within- from between-person changes.Method: We examined whether week-to-week changes in cognitive reappraisal and expressive suppression (Emotion Regulation Questionnaire) predicted subsequent changes in distress (Kessler-6 Psychological Distress Scale) and well-being (WHO-5 Well-being Index) pooling two trials of a guided self-help CBT acceptance and commitment therapy. We used a random-intercept cross-lagged panel model to disaggregate between- vs. within-person changes. Participants (N = 274) were 82% female, 80% non-Hispanic White, and mean age 37 years (SD = 13). Results: At the within-person level, week-to-week changes in reappraisal were consistently associated with subsequent changes in distress, but less consistently with well-being. Changes in expressive suppression were less consistently associated with subsequent changes in distress or well-being. The associations between reappraisal and distress were robust to different model specifications, including detrending. Conclusions: Cognitive reappraisal may be involved in the process of change in CBTs, including so-called “third-wave” CBTs, which have traditionally de-emphasized its importance.Keywords: cognitive behavioral therapy; emotion regulation; mechanisms; digital mental health interventions; guided self-help

Introduction: Posttraumatic stress disorder (PTSD) frequently co-occurs with other mood and anxiety disorders. The contrast avoidance (CA) model proposes that individuals maintain anxiety to avoid large negative emotional shifts following aversive events. Although CA has been studied primarily in generalized anxiety disorder, its role in posttraumatic stress symptoms (PTSSs) remains largely unexplored. This study examined the association between CA, PTSSs, and other anxiety disorder vulnerability constructs.Methods: Participants (N = 1,186) completed an online survey assessing PTSSs, CA, and established PTSD risk factors. Model-based random forests identified the strongest predictors of PTSS severity. Bootstrapped AIC was used to identify significant two-way interactions, which were subsequently examined using simple slopes analyses.Results: CA significantly interacted with threat beliefs (b = 0.77, p < .001), such that threat beliefs were more strongly associated with PTSSs at higher levels of CA. Contrast avoidance also interacted with trust beliefs (b = –0.59, p < .001), indicating that greater trust in others attenuated the association between CA and PTSSs. A borderline interaction emerged between CA and behavioral avoidance (b = –0.46, p = .058). Model-based recursive partitioning suggested that cognitive vulnerabilities, particularly threat beliefs, amplified the effects of CA more consistently than affective or behavioral risk factors.Conclusions: Contrast avoidance is an important correlate of PTSSs, particularly when paired with cognitive vulnerabilities. Future longitudinal and experimental research should clarify causal mechanisms and evaluate whether interventions targeting CA and maladaptive trauma-related cognitions can reduce PTSS severity.

An empirically derived system for classifying psychopathology has long been a goal in psychiatric research. The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been criticized for high comorbidity and heterogeneous symptom presentations among DSM diagnoses. Competing alternative models include the Hierarchical Taxonomy of Psychopathology, a quantitative, dimensional alternative, and network theories, which conceptualize mental disorders as emergent from dynamic interactions of symptoms and mechanisms. Fisher (see record 2026-72441-001) introduced a set-theoretic framework to identify minimally sufficient symptom sets that are statistically predictive of outcomes such as psychopathology-related distress or impairment. Using analyses of symptoms of major depression, generalized anxiety, and posttraumatic stress disorder, Fisher demonstrated how specific features (e.g., uncontrollable worry) emerge as necessary within sufficient sets that predict validators. In essence, these analyses suggest that a lot of the heterogeneity in DSM disorders can be reduced by a set-theoretic approach. The analysis has several limitations (e.g., validators, skip-logic in interviews). Nonetheless, the method has several desirable properties: It is empirically grounded, accommodates individual variability in symptoms, and retains categorical features for clinical application. Conceptually, it exemplifies how mathematical tools from outside psychology can enrich nosological research. We recommend methodological pluralism to study the classification of psychopathology and also caution readers to test assumptions about the relative merits of diagnostic systems that compete with the DSM. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Children and adolescents spend a large amount of time on social media, where they often seek health information. Growing research suggests misinformation is common across various domains of health, such as infectious disease and men's sexual health. Burgeoning evidence suggests that misinformation about mental health may be common as well, especially with regard to trauma and neurodevelopmental diagnoses. There is a need to understand the real‐world context that facilitates the spread of misinformation as well as what features of the online environment promote misinformation. Researchers and clinicians working with children and adolescents must consider the role of misinformation in their work. All mental health professionals need to disseminate accurate, helpful, and engaging information about mental health and mental health treatments.

Digital, self-guided, single-session interventions (SSIs) offer a structured psychological intervention within one interaction. We crowdsourced 66 diverse 10-minute SSIs for depression and selected 11 for testing in a pre-registered experiment (ClinicalTrials.gov ID: NCT06856668). American adults (N = 7,505) experiencing elevated depressive symptoms were recruited online and randomly assigned to one of the 11 crowdsourced SSIs, a previously-validated active comparison SSI, or a control without intervention content. Nearly all SSIs boosted agency and hope for improvement immediately after completion (ds ≤ 0.37). However, only two SSIs significantly reduced depression at four-week follow-up (ds = 0.14 and 0.15). Unexpectedly, some SSIs may have decreased readiness to change at four weeks (ds ≤ 0.14). The most successful SSIs provided memorable, engaging, and actionable guidance on a skill that directly addressed users’ struggles. Future work should aim to leverage SSIs’ short-term gains to promote sustained behavior change or service engagement.