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OBJECTIVE: Cognitive therapy (CT) skills are an index of treatment progress. They predict changes in patients’ acute depressive symptoms and symptom relapses. However, the psychometric properties of the various measures of CT skills are poorly understood. This study aimed to investigate the factor structure of the Competencies of Cognitive Therapy Scale–Self Report (CCTS-SR) and assess its concurrent validity. METHODS: The psychometric properties of the CCTS-SR were explored by using data from a panel of online respondents (N=410). The fit of a one-factor solution was explored by using a confirmatory factor analysis. Exploratory bifactor analyses (EBFA) were then conducted to determine other possible factor structures. RESULTS: The one-factor solution did not fit the data well. Results of the EBFA suggested that the factor structure of the CCTS-SR may be characterized by a single underlying dimension capturing the general use of CT skills as well as by more specific factors the authors labeled “behavioral activation” and “CT comprehension.” The variance captured by the factor initially labeled as CT comprehension was correlated with measures of depression and emotional dysregulation, suggesting that these items do not capture CT comprehension and should be removed from the scale. CONCLUSIONS: The CCTS-SR seems to be characterized by more than a single factor, and items that seemingly compose CT comprehension (i.e., items 13 and 14) may need to be removed. Although the CCTS-SR may be a valid index of therapy progress, more attention needs to be paid to its psychometric properties.

Trauma exposure and posttraumatic stress symptoms (PTSS) are associated with biases in emotional face processing. Existing research has utilized a variety of methodological techniques to demonstrate hyperreactivity to threatening cues in posttraumatic stress disorder (PTSD; i.e., fearful faces), but studies to date have shown conflicting findings, including both increased and decreased time fixating on fearful faces. Moreover, the impact of PTSS severity on emotional face processing in the general population is unknown, as the generalizability of prior work is limited. The current study aimed to examine the associations between PTSS and sensitivity to detecting differences in fearful, angry, and happy faces in a large international sample. Participants were 1,182 visitors (M

This study examines the severity of generalized anxiety disorder (GAD) symptoms among sexual minorities in a large international sample and analyzes the relationship between GAD symptoms and two nation-level policies discriminating against sexual minorities: marriage inequality and criminalization of same-sex sex acts. Members of sexual minorities show higher rates of GAD. Structural stigma may contribute to poor mental health outcomes among sexual minorities. We collected GAD symptoms in a sample of 5,929 participants from countries with differing policies restricting the rights of sexual minorities between May 2018 and October 2018. We then used mixed effects regression to examine the effects of sexual orientation and structural stigma (marriage inequality and criminalization of same-sex sex acts) on GAD symptoms. Participants reporting same-sex and both-sex attraction showed more anxiety symptoms than participants reporting opposite-sex attraction. There was no significant interaction effect between marriage inequality or criminalization of same-sex sex acts and sexual orientation on GAD symptoms. The higher measured anxiety among sexual minority participants is consistent with previous research; however, the absence of an interaction effect between sexual orientation and discriminatory policies was contrary to our predictions. We hypothesize that this could be due to differential concealment of sexual orientation due to structural stigma or the effects of stigma and discrimination outside of policy. These findings suggest that changing discriminatory policies may not be sufficient to improve mental health outcomes among sexual minorities; instead, it may be necessary to address sources of stigma beyond direct legal policies.

BACKGROUND Like the COVID-19 pandemic, the recent global monkeypox outbreak was characterized by the rising prevalence of public health misinformation on social media. This highlights the continuing challenges faced by public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the monkeypox outbreak to observe the tension between rapidly diffusing misinformation and public health communication. OBJECTIVE To analyze Twitter discussions surrounding the global monkeypox outbreak beginning in May-July 2022. METHODS We collected all monkeypox-related Tweets posted between May 7, 2022 and July 23, 2022. We then applied Sentence Bi-directional Encoder from Transformers (S-BERT) to tweet content to generate representations of their content in high-dimensional vector space where semantically similar tweets will be located closely together. We project the set of tweet embeddings to a two-dimensional map by applying Principal Component Analysis (PCA) and Uniform Manifold Approximation Projection (UMAP). Finally, we group these datapoints into 7 topical clusters using k-means clustering and analyze each cluster to determine their dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal changes in the data. RESULTS We discovered 7 distinct clusters of topical content. 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 monkeypox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the World Health Organization (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.

Mobile- and web-based psychological research are a valuable addition to the set of tools available for scientific study, reducing logistical barriers for research participation and allowing the recruitment of larger and more diverse participant groups. However, this comes at the cost of reduced control over the technology used by participants, which can introduce new sources of variability into study results. In this study, we examined differences in measured performance on timed and untimed cognitive tests between users of common digital devices in 59,587 (Study 1) and 3818 (Study 2) visitors to TestMyBrain.org , a web-based cognitive testing platform. Controlling for age, gender, educational background, and cognitive performance on an untimed vocabulary test, users of mobile devices, particularly Android smartphones, showed significantly slower performance on tests of reaction time than users of laptop and desktop computers, suggesting that differences in device latency affect measured reaction times. Users of devices that differ in user interface (e.g. screen size, mouse vs. touchscreen) also show significant differences (p

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 avenue in which to collect large sample of longitudinal data from individuals to study psychopathology. Nonetheless, there are concerns regarding whether people who share their social media data for research are significantly different from people who do not. OBJECTIVE We report the results of two large ongoing studies in which we collect Twitter data and self-reported clinical screening scales, the Studies of Online Cohorts for Internalizing symptoms and Language (SOCIAL). We categorized individuals based on whether they were deemed to have given a valid Twitter account. We described differences in sociodemographic features, clinical symptoms, and aspects of social media use by whether or not individuals gave valid accounts. METHODS Participants were a nationally representative sample of Twitter-using adults (SOCIAL-I: N= 1,121) as well as a sample of college students in the Midwest (SOCIAL-II: N= 2,015), of which 61% were Twitter users. For all participants who were Twitter users, we asked for access to their Twitter handle which we analyzed with BotOMeter, an online application rating the likelihood the account belongs to a bot. We divided participants into four groups: 1) Twitter users who did not allow access to their account (“No handle”), 2) those who denied being Twitter users (“No Twitter,” only available for SOCIAL-II), 3) Twitter users who gave their handles but whose account had high BotOMeter score (“Bot-like”), and 4) Twitter users who provided their handles and had low BotOMeter scores (“Valid”). RESULTS n SOCIAL-I, most individuals were classified as valid (n=580) and few were deemed bot-like (n=190). 351 gave no handle. In SOCIAL-II, many individuals were not Twitter users (n = 760). Of the Twitter users in SOCIAL-II (n = 1, 455), most were classified as either invalid (n = 515) or valid (n = 484), with a smaller fraction deemed bot-like (n = 229). Participants reported high rates of mental health diagnoses as well as high levels of symptoms, especially in SOCIAL-II. In general, differences between individuals who provided or did not provide their social media handle were small and not statistically significant CONCLUSIONS Triangulating passively-acquired social media data and self-reported questionnaires offers avenues for large-scale assessment and evaluation of vulnerability to mental disorders. The propensity of participants to share social media handles is not likely a source of sample bias in subsequent social media analytics

BACKGROUND In recent years, social media has become a rich source of mental health data. However, there is a lack of research on the accuracy and validity of self-reported diagnostic information online. 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 analysis. We hypothesized that self-reported diagnoses would correspond to validated disorder-specific severity questionnaires across two large online samples. METHODS Study 1 participants were 1123 adults from a national Qualtrics panel (mean age= 34.65, SD= 12.56; 56.65% female). Study 2 participants were 2237 college students from a large university in the Midwest (mean age= 19.75, SD= 2.75; 75.25% female). All participants completed an online survey about their mental health, social media use, and demographic information. Additionally, participants reported on 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,” and “No” for each condition. We conducted a series of analysis of variance (ANOVA) tests to determine whether there were differences between each of the four diagnostic groups and used post-hoc Tukey tests to examine the nature of the differences. RESULTS In Study 1, for self-reported mania (F(3, 1097)=2.75,P=.04) somatic symptom disorder (F(3, 1060)=26.75,P< .001) and alcohol use disorder F(3, 1097)=77.73,P< .001), the pattern of mean differences did not suggest that individuals are accurate in their insight to diagnoses. In Study 2, for all disorders but bipolar disorder (F(3, 659)=1.43,P= .23), ANOVA results were consistent with our expectations. Across both studies and for most conditions assessed, individuals who say they have been diagnosed with a disorder had the highest severity scores on self-report questionnaires, but that is closely followed by individuals who have not been diagnosed but believe they should be diagnosed. This was especially true for depression, generalized anxiety, and insomnia. For mania and bipolar disorder, questionnaire scores did not differentiate individuals who had been diagnosed from those who had not. CONCLUSIONS In general, if an individual believes they should be diagnosed with a 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 like depression and generalized anxiety disorder. Researchers can put more faith into patient self-report, including those that occur in online experiments such as social media posts, when individuals report diagnoses of depression and anxiety disorders. Replication and further study is recommended.

Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.Cognitive distortions have a central role in the development of depression. Here, the authors examine the tweets of thousands of individuals who report a diagnosis of depression, revealing significantly higher levels of distortions compared with a control group of Twitter users.

Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.

BACKGROUND: Depression is associated with biases in facial emotion processing, which have an impact on the course and treatment of depression. While decades of research have established a negativity bias in processing in depression, there is still a gap in our understanding of how depression severity impacts sensitivity to detecting differences in emotional faces. METHODS: We examined emotion sensitivity (ES), or the ability to to detect subtle differences in emotional faces, in a large, geographically and culturally diverse, web-based sample (N = 6,598, age range = 18 – 96, 56.50% female, 66% Caucasian). Participants completed ES tasks (fear, anger, or happiness) and a Beck Depression Inventory-II, to determine depression severity. RESULTS: Depression severity was correlated with overall ES performance as well as ES performance for individual emotions. Higher depression scores were associated with poorer performance in detecting happiness, fear, and anger (ps < .001). Examining performance by region, Eastern countries showed significantly poorer ES performance compared to Western countries, and were significantly more depressed. LIMITATIONS: Our sample is non-clinical and self-selected. CONCLUSIONS: This study is an extension of existing research on emotional facial processing, with an approach that takes into consideration the heterogeneity of depression symptoms and corrects psychometric confounds of traditional emotion face processing paradigms. Overall, factors related to severity, task reliability, and facial stimuli should be considered in determining the potential mechanism of facial emotion processing in the onset and course of depression.

Objectives:Cognitive impairments are directly related to severity of symptoms and are a primary cause for functional impairment. Intraindividual cognitive variability likely plays a role in both risk and resiliency from symptoms. In fact, such cognitive variability may be an earlier marker of cognitive decline and emergent psychiatric symptoms than traditional psychiatric or behavioral symptoms. Here, our objectives were to survey the literature linking intraindividual cognitive variability, trauma, and dementia and to suggest a potential research agenda.Design:A wide body of literature suggests that exposure to major stressors is associated with poorer cognitive performance, with intraindividual cognitive variability in particular linked to the development of posttraumatic stress disorder (PTSD) in the aftermath of severe trauma.Measurements:In this narrative review, we survey the empirical studies to date that evaluate the connection between intraindividual cognitive variability, PTSD, and pathological aging including dementia.Results:The literature suggests that reaction time (RT) variability within an individual may predict future cognitive impairment, including premature cognitive aging, and is significantly associated with PTSD symptoms.Conclusions:Based on our findings, we argue that intraindividual RT variability may serve as a common pathological indicator for trauma-related dementia risk and should be investigated in future studies.

Face emotion perception is important for social functioning and mental health. In addition to recognizing categories of face emotion, accurate emotion perception relies on the ability to detect subtle differences in emotion intensity. The primary aim of this study was to examine participants' ability to discriminate the intensity of facial emotions (emotion sensitivity: ES) in three psychometrically matched ES tasks (fear, anger, or happiness), to identify developmental changes in sensitivity to face emotion intensity across the lifespan. We predicted that increased age would be associated with lower anger and fear ES, with minimal differences in happiness ES. Participants were 9,546 responders to a Web-based ES study (age range = 10 to 85 years old). Results of segmented linear regression confirmed our hypotheses and revealed differential patterns of ES based on age, sex, and emotion category. Females showed enhanced sensitivity to anger and fear relative to males, but similar sensitivity to happiness. While sensitivity to all emotions increased during adolescence and early adulthood, sensitivity to anger showed the largest increase, potentially related to the importance of anger perception during adolescent development. We also observed age-related decreases in both anger and fear sensitivity in older adults, with little to no change in happiness sensitivity. Unlike previous studies, the effect observed here could not be explained by task-related confounds (e.g., ceiling effects for happiness recognition), lending strong support to observed differences in ES for happiness, anger, and fear across age. Implications for everyday functioning and the development of psychopathology across the lifespan are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In their critique, Schmidt et al. (1) claim that our analysis of book language (2) cannot meaningfully reflect society. Their arguments bear no relevance to our paper. The statement that “words in books are not clinical interviews, and word frequencies are not psychiatric assessments” is irrelevant because we make no attempts at clinical diagnoses. Their observation that “Derrida” is a more frequent book word than “The Beatles” is also a red herring: We do not compare between words, but instead follow the dynamics of phrases over time. Lastly, our tracking of cognitive distortions (CDS) markers is not an attempt to identify “negative thoughts” but rather to detect markers of language involved in the expression of distorted thinking. Furthermore, Schmidt et al. (1) claim that our … [↵][1] 1To whom correspondence may be addressed. Email: jbollen{at}indiana.edu. [1]: #xref-corresp-1-1

Background : Previous work has indicated that differences in neurocognitive functioning may predict the development of adverse post-traumatic neuropsychiatric sequelae (APNS). Such differences may be vulnerability factors or simply correlates of APNS-related symptoms. Longitudinal studies that measure neurocognitive functioning at the time of trauma are needed to determine whether such differences precede the development of APNS. Methods : Here, we present findings from a subsample of 666 ambulatory patients from the AURORA (A dvancing U nderstanding of R ec O very afteR traum A ) study. All patients presented to EDs after a motor vehicle collision (MVC). We examined associations of neurocognitive test performance shortly after MVC with peritraumatic symptoms in the ED and APNS (depression, post-traumatic stress, post-concussive symptoms, and pain) 2 weeks and 8 weeks later. Neurocognitive tests assessed processing speed, attention, verbal reasoning, memory, and social perception. Results : Distress in the ED was associated with poorer processing speed and short-term memory. Poorer short-term memory was also associated with depression at 2 weeks post-MVC, even after controlling for peritraumatic distress. Finally, higher vocabulary scores were associated with pain 2 weeks post-MVC. Limitations : Self-selection biases among those who present to the ED and enroll in the study limit generalizability. Also, it is not clear whether observed neurocognitive differences predate MVC exposure or arise in the immediate aftermath of MVC exposure. Conclusions : Our results suggest that processing speed and short-term memory may be useful predictors of trauma-related characteristics and the development of some APNS, making such measures clinically-relevant for identifying at-risk individuals.

In non-clinical community samples, older adults are less accurate at identifying others’ emotional expressions than younger adults. Less is known about the effects of age on facial emotion recognition (ER) in clinical samples. We explored ER in outpatients presenting for assessment/treatment of emotional disorders. We predicted that depression severity would be associated with slower, less accurate ER. We expected to find an effect for age, with younger adults showing superior ER performance for negative emotions (fear, anger, sadness). 644 outpatients (57.6% female, M age 31.31, range = 18–76) completed an ER task and a self-report measure of depression severity, the Beck Depression Inventory (BDI-II). The BDI-II was rescored (BDI-R), using only the 10 items specific to unipolar depression, as determined by factor analysis. 34.6% of participants had a clinical depressive disorder. All other participants had a clinical anxiety disorder or not otherwise specified mood disorder. Depression severity, based on the BDI-R, did not predict ER accuracy or intensity for any face type. Age was associated with lower ER accuracy and higher intensity scores for sad and fearful faces, and lower overall accuracy of ER. In a large, transdiagnostic clinical sample of adults with emotional disorders, we found a decline in ER performance with age, particularly for negative emotions (sadness, fear). There was no significant association between depression and ER in our sample, suggesting the importance of considering additional variables such as state affect, attention, and arousal.

Background The COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups. Purpose This study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas. Methods We used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities. Results Longitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001). Discussion Our results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19. Conclusion The COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.

Emotion regulation is a central task of daily life. Difficulty regulating emotions is a core feature of borderline personality disorder (BPD), one of the most common and impairing personality disorder diagnoses. While anger and symptoms of depression are instantiated in the criteria for BPD, anxiety is not, despite being among the most common psychiatric symptoms. In a sample of online respondents (N = 471), we explored the interactions between anxiety symptoms and BPD traits in predicting well-being (WHO-5) as well as poorer work and social adjustment (WSAS), while controlling for anger and depression. We hypothesized that anxiety would lead to more impairment (i.e., lower well-being and poorer work and more difficulties with work and social adjustment) as BPD traits increased. BPD traits and symptoms of anxiety both contributed to overall lower levels well-being and higher levels of psychosocial dysfunction. However, contrary to our expectations, at higher (vs. lower) levels of BPD traits, symptoms of anxiety were less conducive to lower well-being on the WHO-5. For the WSAS, there was no consistent evidence for an interaction between BPD traits and anxiety in predicting functioning. By and large, our results do not support the idea that anxiety contributes to more impairment at higher levels of BPD traits.

Significance Can entire societies become more or less depressed over time? Here, we look for the historical traces of cognitive distortions, thinking patterns that are strongly associated with internalizing disorders such as depression and anxiety, in millions of books published over the course of the last two centuries in English, Spanish, and German. We find a pronounced “hockey stick” pattern: Over the past two decades the textual analogs of cognitive distortions surged well above historical levels, including those of World War I and II, after declining or stabilizing for most of the 20th century. Our results point to the possibility that recent socioeconomic changes, new technology, and social media are associated with a surge of cognitive distortions., Individuals with depression are prone to maladaptive patterns of thinking, known as cognitive distortions, whereby they think about themselves, the world, and the future in overly negative and inaccurate ways. These distortions are associated with marked changes in an individual’s mood, behavior, and language. We hypothesize that societies can undergo similar changes in their collective psychology that are reflected in historical records of language use. Here, we investigate the prevalence of textual markers of cognitive distortions in over 14 million books for the past 125 y and observe a surge of their prevalence since the 1980s, to levels exceeding those of the Great Depression and both World Wars. This pattern does not seem to be driven by changes in word meaning, publishing and writing standards, or the Google Books sample. Our results suggest a recent societal shift toward language associated with cognitive distortions and internalizing disorders.

BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a significant challenge to the world’s mental health and healthcare systems. Considering traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Significant pluralities of the US population now use social media platforms, such as Twitter, to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field from which to understand personal wellbeing.PurposeBroadly, this study answers three research questions: RQ1: What themes emerge from a corpus of US tweets about COVID-19?; RQ2: To what extent does social media use increase during the onset of the COVID-19 pandemic?; and RQ3: Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English-language US tweets collected from an open-access public repository in three steps1. First, we characterized the evolution of hashtags over time using Latent Dirichlet Allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n = 354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment tool2.ResultsLDA topics generated in the early months of the dataset corresponded to major COVID-19 specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted towards US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis sentiment scores of user timelines were initially high and stable, but decreased significantly, and continuously, by late March.Discussion & ConclusionOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased usage rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media usage on mental health, for many social media may further exacerbate negative feelings in the long-term. Thus, considering the overburdened US mental healthcare structure, these findings have important implications for ongoing mitigation efforts.

BACKGROUND Most American adolescents have access to smartphones, and recent estimates suggest that they spend considerable time on social media compared with other physical and leisure activities. A large body of literature has established that social media use is related to poor mental health, but the complicated relationship between social media and symptoms of depression and anxiety in adolescents is yet to be fully understood. OBJECTIVE We aim to investigate the relationship between social media use and depression and anxiety symptoms in adolescents by exploring physical activity as a mediator. METHODS A Qualtrics survey manager recruited adult panel participants between February and March 2019, who indicated that they had adolescent children who spoke English. A total of 4592 adolescent-parent dyads completed the survey that took approximately 39 minutes. The survey entailed completing web-based questionnaires assessing various aspects of social media use, psychological symptoms, and psychosocial factors. The average age of the adolescent participants was 14.62 (SD 1.68; range 12-17) years, and the majority of the adolescent sample was male (2392/4592, 52.09%). RESULTS Total social media use was associated with more depressive symptoms (multiple R2=0.12; F3,4480=207.1; PR2=0.09; F3,4477=145.6; PR2=0.06; F3,4512=98.06; PR2=0.24; F5,4290=266.0; P CONCLUSIONS In a nationally representative sample of adolescents, more social media use was associated with more severe symptoms of depression, anxiety, and loneliness. Increased physical activity was associated with decreased depression and anxiety symptoms. Physical activity partially mediated the relationship between social media use and depression and anxiety. As this was a cross-sectional study, we cannot conclude that social media use causes internalizing symptoms or that physical activity leads to decreased internalizing symptoms—there may be additional confounding variables producing the relationships we observed. Physical activity may protect against the potentially harmful effect of social media on some adolescents. The effect sizes were small to medium, and the results should be interpreted with caution. Other limitations of this study include our reliance on self-reporting. Future work should examine social media use beyond how much time adolescents spend using social media and instead focus on the nature of social media activity.

Human sleep/wake cycles follow a stable circadian rhythm associated with hormonal, emotional, and cognitive changes. Changes of this cycle are implicated in many mental health concerns. In fact, the bidirectional relation between major depressive disorder and sleep has been well-documented. Despite a clear link between sleep disturbances and subsequent disturbances in mood, it is difficult to determine from self-reported data which specific changes of the sleep/wake cycle play the most important role in this association. Here we observe marked changes of activity cycles in millions of twitter posts of 688 subjects who explicitly stated in unequivocal terms that they had received a (clinical) diagnosis of depression as compared to the activity cycles of a large control group (n = 8791). Rather than a phase-shift, as reported in other work, we find significant changes of activity levels in the evening and before dawn. Compared to the control group, depressed subjects were significantly more active from 7 PM to midnight and less active from 3 to 6 AM. Content analysis of tweets revealed a steady rise in rumination and emotional content from midnight to dawn among depressed individuals. These results suggest that diagnosis and treatment of depression may focus on modifying the timing of activity, reducing rumination, and decreasing social media use at specific hours of the day.

Decades of research have established a link between emotional disorders and attentional biases for emotional stimuli, but the relationship between symptom severity and visual attention is still not fully understood. Depression has been associated with increased attention towards dysphoric stimuli and decreased attention on positive stimuli (“negativity bias”), and some studies have also shown this trend in anxiety disorders. We examined eye fixation variables in 47 participants with emotional disorders completing an emotion recognition task. Results showed that depression severity was not associated with increased fixations on dysphoric stimuli, however, higher levels of generalized anxiety predicted increased fixations in the mouth region of sad and happy faces. Higher levels of social interaction anxiety predicted reduced fixations in the eye region of happy faces. While we did not replicate the negativity bias that has been shown in prior studies, our sample was highly comorbid, indicating the need to consider comorbidity, disorder severity, and the task itself when conducting research on visual attention in clinical samples. Additionally, more attention should be paid to the mouth region of emotional faces, as it may provide more specific information regarding the visual processing of emotions.

Mobile- and web-based psychological research are a valuable addition to the set of tools available for scientific study, reducing logistical barriers for research participation and allowing the recruitment of larger and more diverse participant groups. However, this comes at the cost of reduced control over the technology used by participants, which can introduce new sources of variability into study results. In this study, we examined differences in measured performance on timed and untimed cognitive tests between users of common digital devices in 59,587 (Study 1) and 3,818 (Study 2) visitors to TestMyBrain.org, a web-based cognitive testing platform. Controlling for age, sex, and cognitive performance on an untimed vocabulary test, users of mobile devices, particularly Android smartphones, showed significantly slower performance on tests of reaction time than users of laptop and desktop computers, suggesting that differences in device latency effect measured reaction times. Users of devices that differ in user interface (e.g. screen size, mouse vs. touchscreen) also show significant differences (p < 0.001) in measured performance on tests requiring fast reactions or fine motor movements. By quantifying the contribution of device differences to measured cognitive performance in an online setting, we hope to improve the accuracy of mobile- and web-based cognitive assessments, allowing the strengths of these methods to be effectively used.

BACKGROUND The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. OBJECTIVE This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? METHODS We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. RESULTS LDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. CONCLUSIONS Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Background: We examined the availability and components of internet-basedcognitive-behavioral therapies (iCBTs) for depression tested in randomized-controlled trials (RCTs).Objectives: 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.Methods: A literature review of randomized controlled trials for iCBTs was conducted on July 30th, 2021. In each iCBT, interventions were disaggregated into treatment modules, comporting to prior work, and compared to commercially available smartphone apps.Results: Our search yielded 80 studies that used 41 unique iCBTs. Of these, only six (15%) were completely available to the public, more than half were not publicly available (46%), and the remaining 39% were available but had some restrictions (e.g., those based on the user’s geographical location). When comparing iCBTs 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.Conclusions: iCBTs contain evidence-based content, but few are available to the public. Extending beyond efficacy, attention should be paid to the dissemination of iCBTs.

Parker and colleagues developed the Sydney Melancholia Prototype Index (SMPI), a 24-item measure to assess a potential subtype of depression: melancholia. While research supports the validity of the measure, no study has assessed its psychometric properties. We recruited 1633 participants online, of whom 487 reported a lifetime period of depressed mood or anhedonia and were administered the SMPI. We conducted confirmatory factor analyses (CFA) of the SMPI, to assess the proposed fit of the measure. We also conducted exploratory factor analyses (EFA) to explore the structure implied by the current data. CFA did not support the hypothesized factor structure of the SMPI, no matter what structure we assumed as primary (i.e., a one factor, two factor, or bifactor model). An EFA suggested a five-factor solution wherein several items did not appear to co-vary reliably and other factors captured the severity of melancholic symptoms, negative mood reactivity, positive mood reactivity, emotionality and family relationships, and early life adversity. The SMPI may not measure a single construct. Future research should explore the longitudinal association between depression severity, contaminant symptoms, positive and negative mood reactivity, and early life experiences

Digital technology has become a major target area for the development of assessments that can be deployed through mobile devices, across large cohorts, and in naturalistic environments. Here, we summarize the results of a report commissioned by the National Institute of Mental Health (HHSN271201700776P) to evaluate mobile assessments of cognition and their appropriateness for deployment in a field test battery. Using data from over 100,000 participants tested through our digital research platform, TestMyBrain.org , we analyze the appropriateness of 25 standard tests of cognition and information processing for field test battery use. Measures are evaluated in terms of their psychometric properties, validity, engagement, and sensitivity to variations in device hardware and software. We also define a minimum duration for acceptable reliability (minDAR) across all 25 tests, operationalized as the duration a test needs to be to achieve an internal reliability of at least 0.7 for primary outcome measures. We note that many tests adapted from experimental approaches, particularly those involving aspects of positive and negative valence, need further development to achieve acceptable length and reliability (based on very high minDAR values, e.g. 180 minutes vs 3 minutes for threat biases in memory vs. memory alone). Device variability also presents a confound for reaction time tests (e.g. iOS vs Android Cohen’s d = 0.4 for simple reaction time, p < 0.001). Areas of focus for development of such measures are described. Digital cognitive assessment is a promising methodology for large sample studies at relatively low cost. There are notable areas where further research and development work is needed, however, to fully realize the potential for cognitive phenotyping at scale.

The current study used double-blind, placebo-controlled design to examine the effect of intranasal oxytocin (OT) on emotion recognition (ER) and visual attention in 60 outpatients presenting for assessment and treatment of emotional disorders. Our primary hypothesis was that OT would improve recognition of happy faces in depressed participants. The main effect of OT on ER accuracy, speed, and proportion of fixations in the eye region was not significant. Diagnostic group (i.e., presence/absence of a depressive disorder) moderated the effect of OT on ER, but not as expected: OT significantly slowed ER speed for all emotions in participants with anxiety disorders, but did not affect performance in participants with depressive disorders. Depressed participants fixated significantly less in the eye region of sad faces than anxious participants. Before OT can be used to target ER biases, additional research is needed to explicate the differential impact of OT on ER speed in patients with anxiety versus mood disorders.

BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.ObjectiveThis study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.ResultsLDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.ConclusionsOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Outcomes for people with schizophrenia-spectrum disorders (SSDs) are generally poor, making it important to understand risk states and illness transition. The network approach, which conceptualizes psychopathology as a network of causally interacting symptoms, may hold promise in this regard. Here, we present a network analysis of schizotypal personality traits (i.e., schizophrenia-like cognitive, perceptual, affective, interpersonal, and behavioral anomalies that may index one's vulnerability for a SSD) using an international sample. We analyzed data from 9505 participants between the ages of 14-70 who completed the Schizotypal Personality Questionnaire-Brief on TestMyBrain.org. In line with other research, we find that the network of schizotypal traits is densely connected, characterized by three communities of items-interpersonal (I), disorganized (D), cognitive-perceptual (CP)-with I and D features exhibiting the greatest centrality (z-scored M strength: I = 0.56, D = 0.29, CP = -0.84; expected influence: I = 0.54, D = 0.33, CP = -0.84) and predictability (M I = 0.37, D = 0.43, CP = 0.23). Importantly, within our sample, we found the estimated network to be replicable (Network Comparison Test: network structure difference: M = 0.304, p = .420; global strength difference: S = 0.904, p = .530), and estimates of node centrality to be stable (correlation-stability coefficient = 0.75). Further, we find network differences between certain groups differing in levels of SSD risk as a function of age (network structure: difference M = 0.562, p .001; global strength difference: S = 3.483, p = .012) and ethnic minority status (global strength difference: S = 11.488, p = .004). Together, these findings demonstrate the utility of using network approaches to understand SSD risk states as well as the replicability of network findings on schizotypal personality traits and related SSD risk concepts.

Outcomes for people with schizophrenia-spectrum disorders (SSDs) are generally poor making it important to understand risk states and illness transition. The network approach, which conceptualizes psychopathology as a network of causally interacting symptoms, may hold promise in this regard. Here, we present a network analysis of schizotypy—the personality organization involving schizophrenia-like phenomena that is thought to index one’s vulnerability for an SSD—using an international, population-based sample. In line with other research, we find that the schizotypy network is densely connected, characterized by three communities of items with interpersonal and disorganized features exhibiting the greatest centrality and predictability. Further, we find network differences between certain groups differing in levels of SSD risk. Together, these findings demonstrate the utility of using network approaches to understand SSD risk states as well as the replicability of network findings on schizotypy and related SSD risk concepts.

Health anxiety, while not a diagnosis, refers to a set of symptoms characterized by preoccupied worry with having or developing a serious medical illness. An individual presenting with interfering and distressing symptoms of health anxiety may receive a DSM-5 diagnosis of illness anxiety disorder (previously known as hypochondriasis) or somatic symptom disorder, based on the amount of physical complaints they have. Unfortunately, health anxiety is often initially misdiagnosed or undiagnosed due to an individual’s belief that their symptoms are solely physical. Health anxiety is costly to society due to lost work productivity and increased use of medical services. However, health anxiety can be successfully treated within a cognitive-behavioral framework. Intervention techniques include psychoeducation, exposure and response prevention, stress management, and mindfulness processes. Health anxiety symptoms have also shown improvement through pharmacological interventions. Chapter highlights include the definition of health anxiety, various treatment approaches and their efficacies, considerations in light of comorbid physical conditions, and case examples. Ultimately, we conclude that health anxiety, while challenging to diagnose and build trust for treatment in the beginning stages, can be treated effectively within a cognitive-behavioral framework through targeting dysfunctional beliefs about illness.

This study examined the psychometric properties of the Generalized Anxiety Disorder Scale-7 (GAD-7) in a sample of 536 outpatients presenting at a specialty clinic for anxiety and mood disorders. A confirmatory factor analysis (CFA) was used to test the unidimensionality of the GAD-7. This model did not fit the data well. The CFA solution was respecified correlating residuals among items assessing somatic symptoms. This respecified model fit the data well. A series of multiple-groups CFAs determined that the measurement properties of the GAD-7 were invariant between sexes. Scale reliability estimates of the GAD-7 were favorable for the full sample, and for males and females. Sensitivity and specificity could not be balanced at any cut-point. Findings attest to the value of this instrument as a dimensional indicator of GAD severity rather than a screening tool for the presence or absence of the disorder in outpatients with anxiety and mood disorders.

Neuroticism and extraversion have been linked to the etiologies and course of anxiety and mood disorders, such that neuroticism is broadly associated with numerous disorders and extraversion is most strongly associated with social anxiety and depression. While previous research has established the broad associations between temperament and emotional disorders, less is known about the specific, proximal factors that are associated with them, and very few studies have situated these risk factors into a larger etiological model that specifies how they may relate to one another. The current study examined the interaction of extraversion and anxiety sensitivity (AS) in predicting social anxiety symptoms in a large, diagnostically diverse clinical sample (N=826). Symptoms were assessed with self-report and dimensional interview measures, and regression analyses were performed examining the main effects and interaction of extraversion and AS (examining both total and lower-order components) on social anxiety. Results showed that at higher levels of AS, the inverse relationship between extraversion and social anxiety was stronger, and the social concerns component of AS is responsible for this effect. This interaction was also observed with regard to depression symptoms, but the interaction was not present after accounting for shared variance (i.e., comorbidity) between depression and social anxiety symptoms. Clinical and theoretical implications of the results are discussed.