The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults.

BACKGROUND: Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs). METHODS: The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period. RESULTS: Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time. LIMITATIONS: Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only. CONCLUSIONS: The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self-guided web-based interventions, but not all persons benefit from web-based interventions. Therefore, it is important to identify whether someone is likely to benefit from web-based PPIs, in order to triage persons who may not benefit from other interventions. In the current study, we used machine learning to predict individual response to a web-based PPI, in order to investigate baseline prognostic indicators of likelihood of response (N = 120). Our models demonstrated moderate correlations (happiness: r Test = 0.30 ± 0.09; depressive symptoms: r Test = 0.39 ± 0.06), indicating that baseline features can predict changes in happiness and depressive symptoms at a 6-month follow-up. Thus, machine learning can be used to predict outcome changes from a web-based PPI and has important clinical implications for matching individuals to PPIs based on their individual characteristics.

The Trajectories of Online Mental Health Information Seeking: Modeling Search Behavior Before and After Completion of Self-report Screens.

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N=7,572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior.

Comparing Transdiagnostic Risk Factors: Predicting Emergence of Significant Depressive, Anxiety, and Substance Abuse Symptoms Among Juvenile Delinquents.

In a 7-year 11-wave study of low-SES adolescents (N = 856, age = 15.98), we compared multiple well-established transdiagnostic risk factors as predictors of first incidence of significant depressive, anxiety, and substance abuse symptoms across the transition from adolescence to adulthood. Risk factors included negative emotionality, emotion regulation ability, social support, gender, history of trauma, parental histories of substance abuse, parental mental health, and socioeconomic status. Machine learning models revealed that negative emotionality was the most important predictor of both depression and anxiety, and emotion regulation ability was the most important predictor of future significant substance abuse. These findings highlight the critical role that dysregulated emotion may play in the development of some of the most prevalent forms of mental illness.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across days. Moreover, MDD is highly heterogeneous, varying considerably across people in both function and form. Recent efforts have examined MDD heterogeneity byinvestigating how symptoms influence one another over time across individuals in a system; however, these efforts have assumed that symptom dynamics are static and do not dynamically change over time. Nevertheless, it is possible that individual MDD system dynamics change continuously across time. Participants (N = 105) completed ratings of MDD symptoms three times a day for 90 days, and we conducted time varying vector autoregressive models to investigate the idiographic symptom networks. We then illustrated this finding with a case series of five persons with MDD. Supporting prior research, results indicate there is high heterogeneity across persons as individual network composition is unique from person to person. In addition, for most persons, individual symptom networks change dramatically across the 90 days, as evidenced by 86% of individuals experiencing at least one change in their most influential symptom and the median number of shifts being 3 over the 90 days. Additionally, most individuals had at least one symptom that acted as both the most and least influential symptom at any given point over the 90-day period. Our findings offer further insight into short-term symptom dynamics, suggesting that MDD is heterogeneous both across and within persons over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Reliability (or lack thereof) of smartphone ecological momentary assessment of visual dot probe attention bias toward threat indices.

BACKGROUND AND OBJECTIVES: Cognitive bias theories posit that generalized anxiety disorder (GAD) and social anxiety disorder (SAD) are entwined with attention bias toward threats, commonly indexed by faster response time (RT) on threat-congruent (vs. threat-incongruent) trials on the visual dot probe. Moreover, although smartphone ecological momentary assessment (EMA) of the visual dot probe has been developed, their psychometric properties are understudied. This study thus aimed to assess the reliability of 8 smartphone-delivered visual dot probe attention bias and related indices in persons with and without GAD and SAD. METHODS: Community-dwelling adults (n = 819; GAD: 64%; SAD: 49%; Mixed GAD and SAD: 37%; Non-GAD/SAD Controls: 24%) completed a five-trial smartphone-delivered visual dot probe for a median of 60 trials (12 sessions x 5 trials/session) and an average of 100 trials (20 sessions x 5 trials/session). RESULTS: As hypothesized, Global Attention Bias Index, Disengagement Effect, and Facilitation Bias had low-reliability estimates. However, retest-reliability and internal reliability were good for Trial-Level Bias Scores (TLBS) (Bias Toward Treat: intra-class correlation coefficients (ICCs) = 0.626-0.644; split-half r = 0.640-0.670; Attention Bias Variability: ICCs = 0.507-0.567; split-half r = 0.520-0.580) and (In)congruent RTs. Poor retest-reliability and internal reliability estimates were consistently observed for all traditional attention bias and related indices but not TLBS. LIMITATIONS: Our visual dot probe EMA should have administered ≥320 trials to match best-practice guidelines based on similar laboratory studies. CONCLUSIONS: Future research should strive to examine attention bias paradigms beyond the dot-probe task that evidenced meaningful test-retest reliability properties in laboratory and real-world naturalistic settings.

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.

The hidden depths of suicidal discourse: Network analysis and natural language processing unmask uncensored expression.

Background: The socially unattractive and stigmatizing nature of suicidal thought and behavior (STB) makes it especially susceptible to censorship across most modern digital communication platforms. The ubiquitous integration of technology with day-to-day life has presented an invaluable opportunity to leverage unprecedented amounts of data to study STB, yet the complex etiologies and consequences of censorship for research within mainstream online communities render an incomplete picture of STB manifestation. Analyses targeting online written content of suicidal users in environments where fear of reproach is mitigated may provide novel insight into modern trends and signals of STB expression. Methods: Complete written content of N = 192 users, including n = 48 identified as potential suicide completers/highest-risk users (HRUs), on the pro-choice suicide forum, Sanctioned Suicide, was modeled using a combination of lexicon-based topic modeling (EMPATH) and exploratory network analysis techniques to characterize and highlight prominent aspects of censorship-free suicidal discourse. Results: Modeling of over 2 million tokens across 37,136 forum posts found higher frequency of positive emotion and optimism among HRUs, emphasis on methods seeking and sharing behaviors, prominence of previously undocumented jargon, and semantics related to loneliness and life adversity. Conclusion: This natural language processing (NLP)- and network-driven exposé of online STB subculture uncovered trends that deserve further attention within suicidology as they may be able to bolster detection, intervention, and prevention of suicidal outcomes and exposures.

Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms.

Wearable technology enables unobtrusive collection of longitudinally dense data, allowing for continuous monitoring of physiology and behavior. These digital phenotypes, or device-based indicators, are frequently leveraged to study depression. However, they are usually considered alongside questionnaire sum-scores which collapse the symptomatic gamut into a general representation of severity. To explore the contributions of passive sensing streams more precisely, associations of nine passive sensing-derived features with self-report responses to Center for Epidemiologic Studies Depression (CES-D) items were modeled. Using data from the NetHealth study on N=469 college students, this work generated mixed ordinal logistic regression models to summarize contributions of pulse, movement, and sleep data to depression symptom detection. Emphasizing the importance of the college context, wearable features displayed unique and complementary properties in their heterogeneously significant associations with CES-D items. This work provides conceptual and exploratory blueprints for a reductionist approach to modeling depression within passive sensing research.

Dysregulated Emotion and Trying Substances in Childhood: Insights from a Large Nationally Representative Cohort Study.

OBJECTIVE: Transdiagnostic perspectives on the shared origins of mental illness posit that dysregulated emotion may represent a key driving force behind multiple forms of psychopathology, including substance use disorders. The present study examined whether a link between dysregulated emotion and trying illicit substances could be observed in childhood. METHOD: In a large (N = 7,418) nationally representative sample of children (Mage = 9.9), individual differences in emotion dysregulation were indexed using child and parent reports of frequency of children's emotional outbursts, as well as children's performance on the emotional N-Back task. Two latent variables, derived from either parental/child-report or performance-based indicators, were evaluated as predictors of having ever tried alcohol, tobacco, or marijuana. RESULTS: Results showed that reports of dysregulated emotion were linked to a greater likelihood of trying both alcohol and tobacco products. These findings were also present when controlling for individual differences in executive control and socioeconomic status. CONCLUSIONS: These results suggest that well-established links between dysregulated negative emotion and substance use may emerge as early as in childhood and also suggest that children who experience excessive episodes of uncontrollable negative emotion may be at greater risk for trying substances early in life.

Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care.

BACKGROUND: Subclinical depression (SD) is a mental health disorder characterized by minor depressive symptoms. Most SD patients are treated in the primary practice, but many respond poorly to treatment at the expense of provider resources. Stepped care approaches are appealing for tiering SD care to efficiently allocate scarce resources while jointly optimizing patient outcomes. However, stepped care can be time inefficient, as some persons may respond poorly and be forced to suffer with their symptoms for prolonged periods. Machine learning can offer insight into optimal treatment paths and inform clinical recommendations for incident patients. METHODS: As part of the Step-Dep trial, participants with SD were randomized to receive stepped care (N=96) or usual care (N=140). Machine learning was used to predict changes in depressive symptoms every three months over a year for each treatment group. RESULTS: Tree-based models were effective in predicting PHQ-9 changes among patients who received stepped care (r=0.35-0.46, MAE=0.14-0.17) and usual care (r=0.34-0.49, MAE=0.15-0.18). Patients who received stepped care were more likely to reduce PHQ-9 scores if they had high PHQ-9 but low HADS-A scores at baseline, a low number of chronic illnesses, and an internal locus of control. LIMITATIONS: Models may suffer from potential overfitting due to sample size limitations. CONCLUSION: Our findings demonstrate the promise of machine learning for predicting changes in depressive symptoms for SD patients receiving different treatments. Trained models can intake incident patient information and predict outcomes to inform personalized care.

Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.

BACKGROUND: Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. OBJECTIVE: The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. METHODS: The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. RESULTS: The participants' average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. CONCLUSIONS: To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3389/fpsyt.2022.871916.

Development of a digital biomarker and intervention for subclinical depression: study protocol for a longitudinal waitlist control study.

BACKGROUND: Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). AIM: With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. METHOD: Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. DISCUSSION: Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).

Second-to-Second Affective Responses to Images Correspond With Affective Reactivity, Variability, and Instability in Daily Life.

Two distinct literatures have evolved to study within-person changes in affect over time. One literature has examined affect dynamics with millisecond-level resolution under controlled laboratory conditions, and the second literature has captured affective dynamics across much longer timescales (e.g., hours or days) within the relatively uncontrolled but more ecologically valid conditions of daily life. Despite the importance of linking these literatures, very little research has been done so far. In the laboratory, peak affect intensities and reaction durations were quantified using a paradigm that captures second-to-second changes in subjective affect elicited by provocative images. In two studies, analyses attempted to link these micro-dynamic indexes to fluctuations in daily affect ratings collected via daily protocols up to 4 weeks later. Although peak intensity and reaction duration scores from the laboratory did not consistently relate to daily scores pertaining to affect variability or instability, the total magnitude of changes in affect following images did display relationships of this type. In addition, higher peaks in the laboratory predicted larger intensity reactions to salient daily events. Together, the studies provide insights into the mechanisms through which correspondences and noncorrespondences between laboratory reactivity indices and daily affect dynamic measures can be expected.

Testing domain knowledge and risk of bias of a large-scale general artificial intelligence model in mental health.

Background: With a rapidly expanding gap between the need for and availability of mental health care, artificial intelligence (AI) presents a promising, scalable solution to mental health assessment and treatment. Given the novelty and inscrutable nature of such systems, exploratory measures aimed at understanding domain knowledge and potential biases of such systems are necessary for ongoing translational development and future deployment in high-stakes healthcare settings. Methods: We investigated the domain knowledge and demographic bias of a generative, AI model using contrived clinical vignettes with systematically varied demographic features. We used balanced accuracy (BAC) to quantify the model's performance. We used generalized linear mixed-effects models to quantify the relationship between demographic factors and model interpretation. Findings: We found variable model performance across diagnoses; attention deficit hyperactivity disorder, posttraumatic stress disorder, alcohol use disorder, narcissistic personality disorder, binge eating disorder, and generalized anxiety disorder showed high BAC (0.70 ≤ BAC ≤ 0.82); bipolar disorder, bulimia nervosa, barbiturate use disorder, conduct disorder, somatic symptom disorder, benzodiazepine use disorder, LSD use disorder, histrionic personality disorder, and functional neurological symptom disorder showed low BAC (BAC ≤ 0.59). Interpretation: Our findings demonstrate initial promise in the domain knowledge of a large AI model, with performance variability perhaps due to the more salient hallmark symptoms, narrower differential diagnosis, and higher prevalence of some disorders. We found limited evidence of model demographic bias, although we do observe some gender and racial differences in model outcomes mirroring real-world differential prevalence estimates.

Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year.

INTRODUCTION: Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population. METHODS: To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15-17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features. RESULTS: The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience. DISCUSSION: This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience.

Evaluating the Impact of Mask Mandates and Political Party Affiliation on Mental Health Internet Search Behavior in the United States During the COVID-19 Pandemic: Generalized Additive Mixed Model Framework.

BACKGROUND: The impacts of the COVID-19 pandemic on mental health worldwide and in the United States have been well documented. However, there is limited research examining the long-term effects of the pandemic on mental health, particularly in relation to pervasive policies such as statewide mask mandates and political party affiliation. OBJECTIVE: The goal of this study was to examine whether statewide mask mandates and political party affiliations yielded differential changes in mental health symptoms across the United States by leveraging state-specific internet search query data. METHODS: This study leveraged Google search queries from March 24, 2020, to March 29, 2021, in each of the 50 states in the United States. Of the 50 states, 39 implemented statewide mask mandates-with 16 of these states being Republican-to combat the spread of COVID-19. This study investigated whether mask mandates were associated differentially with mental health in states with and without mandates by exploring variations in mental health search queries across the United States. In addition, political party affiliation was examined as a potential covariate to determine whether mask mandates had differential associations with mental health in Republican and Democratic states. Generalized additive mixed models were implemented to model associations among mask mandates, political party affiliation, and mental health search volume for up to 7 months following the implementation of a mask mandate. RESULTS: The results of generalized additive mixed models revealed that search volume for "restless" significantly increased following a mask mandate across all states, whereas the search volume for "irritable" and "anxiety" increased and decreased, respectively, following a mandate for Republican states in comparison with Democratic states. Most mental health search terms did not exhibit significant changes in search volume in relation to mask mandate implementation. CONCLUSIONS: These findings suggest that mask mandates were associated nonlinearly with significant changes in mental health search behavior, with the most notable associations occurring in anxiety-related search terms. Therefore, policy makers should consider monitoring and providing additional support for these mental health symptoms following the implementation of public health-related mandates such as mask mandates. Nevertheless, these results do not provide evidence for an overwhelming impact of mask mandates on population-level mental health in the United States.

Mechanisms of cognitive behavioral therapy vs. supportive psychotherapy in body dysmorphic disorder: An exploratory mediation analysis.

Body dysmorphic disorder (BDD) is common, severe, and often chronic. Cognitive behavioral therapy (CBT) is the first-line psychosocial treatment for BDD, with well-established efficacy. However, some patients do not improve with CBT, and little is known about how CBT confers its effects. Neurocognitive processes have been implicated in the etiology and maintenance of BDD and are targeted by CBT-BDD treatment components. Yet, the malleability of these factors in BDD, and their potential role in mediating symptom improvement, are not well understood. Understanding how treatment works could help optimize treatment outcomes. In this secondary data analysis of a randomized clinical trial of CBT vs. supportive psychotherapy (SPT) in BDD (n = 120), we examined whether treatment-related changes in detail processing (Rey-Osterrieth Complex Figure test), maladaptive appearance beliefs (Appearance Schemas Inventory-Revised), and emotion recognition (Emotion Recognition Task) mediated treatment outcome. All constructs improved over time and were associated with symptom improvement. CBT was associated with greater improvements in maladaptive beliefs than SPT. None of the variables examined mediated symptom improvement. Findings suggest that with successful treatment, individuals with BDD demonstrate reduced neurocognitive deficits (detail processing, emotion recognition, maladaptive beliefs) and that CBT is more likely than SPT to improve maladaptive appearance beliefs. More work is needed to understand mechanisms of change and thus maximize treatment outcomes.