Savoring changes novel positive mindset targets of GAD treatment: Optimism, prioritizing positivity, kill-joy thinking, and worry mediation.

This study analyzed effects of savoring on unstudied positive mindset targets of generalized anxiety disorder (GAD) treatment (ClinicalTrials.gov: NCT05040061). 85 students with GAD were randomly assigned to one of two ecological momentary interventions (EMIs) on smartphone for seven days. The SkillJoy EMI promoted practices for savoring positive emotions. An active control EMI mirrored SkillJoy, yet did not include savoring or positive emotion. Optimism, worry, kill-joy thinking (lessening positive emotion with cognition), and prioritization of positive emotion activities and goals were assessed at pre-trial, eighth-day, post-trial, and 30th-day follow-up. Savoring was assessed pre-trial and fifth-day mid-trial. Longitudinal linear mixed models and simple slope analyses examined change between and within conditions. Bias-corrected bootstrapping path analysis examined mediation of worry change by increased savoring. SkillJoy led to significantly greater increases in both optimism and prioritizing positivity than the control from pre-trial to post-trial and pre-trial to follow-up. Both interventions significantly reduced kill-joy thinking at both timepoints with Skilljoy leading to marginally greater change at post-trial. Pre- to mid-trial increases in savoring mediated the relationships between treatment condition and reductions in worry at both post-trial and follow-up.

The therapy relationship, digital interventions, and their interaction.

This article serves as an introduction to a special section devoted to the psychotherapy relationship and digital interventions. The nature of the therapy relationship is explored, and the question is raised as to whether machines can have relationships with their users. Finally, an overview and synthesis of the articles in the special section is provided.

Is a brief mindfulness ecological momentary intervention more efficacious than a self-monitoring app for social anxiety disorder? A randomized controlled trial.

Despite their proliferation, limited knowledge exists regarding possible benefits of brief mindfulness ecological momentary interventions (MEMIs) for social anxiety disorder (SAD). Propositions that MEMIs could alleviate SAD symptoms and related clinical outcomes remain untested. This trial evaluated a 14-day MEMI for SAD. Participants with self-reported SAD were randomized to MEMI (n = 96) or self-monitoring app (SM; n = 95). Whereas MEMI instructed mindfulness exercises, SM prompted only self-monitoring five times daily for 14 days. Participants completed state-level self-reports of depression, anxiety, and mindfulness pre-post-mindfulness practice and SAD symptoms, worry, depression severity, repetitive negative thinking, and trait mindfulness at pre-randomization, post-intervention, and 1-month follow-up (1MFU). Hierarchical linear modeling was conducted. The MEMI yielded statistically significantly larger improvements in momentary depression, anxiety, and mindfulness (Cohen's d = -0.10-0.11). Although no between-group effects emerged in alleviating SAD fear and avoidance, excessive worry, depression severity, repetitive negative thinking, and trait mindfulness (-0.13-0.15), within-group effects were significantly small-to-large from pre-post and pre-1MFU (-4.62-0.67). A significant reduction in depression severity occurred in MEMI (-0.63--0.60) but not SM (-0.31--0.29). Brief MEMI and SM yielded nondifferent sustained effects on SAD, comorbid symptoms, and risk factors, highlighting its potential value within stepped-care delivery settings.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students.

Background: A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion: These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration: Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Nurturing innovation, catalyzing change: Honoring the legacy of Dr. C. Barr Taylor in the eating disorders field.

This Virtual Issue of the International Journal of Eating Disorders honors the legacy of the late Dr. C. Barr Taylor in the eating disorders (EDs) field. For decades, Dr. Taylor led the way in not only conducting the research needed to achieve the ultimate goal of making affordable, accessible, and evidence-based care for EDs available to all, but also nurturing the next generation of scientific leaders and innovators. Articles included in this Virtual Issue are a selection of Dr. Taylor's published works in the Journal in the past decade, spanning original research, ideas worth researching, commentaries, and a systematic review. We hope this Virtual Issue will inspire the next generation of research in EDs, and equally, if not more importantly, the next generation of young investigators in the field. We urge the field to continue and build upon Dr. Taylor's vision-to increase access to targeted prevention and intervention for EDs in innovative and forward-thinking ways-while embracing his unique and powerful mentorship style to lift up early career investigators and create a community of leaders to address and solve our field's biggest challenges.

Introduction to a special issue on the contrast avoidance model.

The current paper introduces the special issue on the Contrast Avoidance Model. The Contrast Avoidance Mmodel theorizes that chronic worriers are afraid of a sharp increase in negative emotion and/or sharp reduction in positive emotion (labeled negative emotional contrasts; NECs). They thus use perseverative thought as means to create and sustain negative emotion to prevent NECs if they were to experience negative events. Further, these individuals are uncomfortable with sustained positive emotion because it leaves them vulnerable to NECs. At the same time, worry increases the probability of positive emotional contrasts (PECs), or sharp increases in positive emotion or decreases in negative emotion when things turn out better than expected or positive events are experienced. Therefore, these individuals generate negative emotion via perseverative thought as a way to avoid NECs and increase the likelihood of PECs. The current special issue provides novel research on the model.

The Contrast Avoidance Model: Conclusion and synthesis of new research in the special issue.

In this special series, new research on the Contrast Avoidance Model (CAM) was presented, including studies on the role of CAM in the maintenance of chronic worry, the incremental validity of CAM, CAM as a mediator of the association between generalized anxiety disorder (GAD) and other variables, CAM as transdiagnostic, and interpersonal behaviors as a means to avoid negative emotional contrasts (NECs). Furthermore, the role of perseverative thought in relation to positive emotional contrasts (PECs) was explored. Studies indicated that higher worry was positively and negatively reinforced, a factor that is likely to contribute to the maintenance of GAD. Further, research demonstrated that CAM contributed unique variance to understanding GAD above and beyond other variables associated with GAD, such as intolerance of uncertainty and negative problem orientation. Additional research revealed the transdiagnostic nature of contrast avoidance, as well as the association between contrast avoidance and problem-solving deficits. In addition, both worry and rumination increased the likelihood of PECs. Further, data suggested that anxious individuals may use interpersonal strategies to avoid NECs. Finally, savoring positive emotions was found to reduce contrast avoidance, providing a novel intervention strategy to address contrast avoidance in individuals with GAD.

Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model.

Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). The final top five prescriptive predictor models of all outcomes performed well (AUC = .752 .886). The following variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. White individuals benefitted more than non-White. PC, TM, EF, and sociodemographic data might help predictive models optimize intervention selection for GAD.

Internalizing psychopathology and its links to suicidal ideation, dysfunctional attitudes, and help-seeking readiness in a national sample of college students.

BACKGROUND: Recent evidence suggests that multiple emotional disorders may be better assessed using dimensional models of psychopathology. The current study utilized a cross-sectional population survey of college students (N = 8613 participants) to examine the extent to which broad psychopathology factors accounted for specific associations between emotional problems and clinical and behavioral validators: suicidality, dysfunctional attitudes, and treatment seeking. METHODS: Confirmatory factor models were estimated to identify the best structure of psychopathology. Models were then estimated to examine the broad and specific associations between each psychopathology indicator and the clinical and behavioral validators. RESULTS: The hierarchical model of psychopathology with internalizing problems at the top, fear, and distress at the second level, and five specific symptom dimensions at the third level evidenced the best fit. The associations between symptom indicators of psychopathology and clinical and behavioral validators were relatively small and inconsistent. Instead, much of the association between clinical and behavioral validators and emotional problems operated at a higher-order level. LIMITATIONS: The cross-sectional nature of the survey precludes the ability to make conclusions regarding causality. CONCLUSIONS: Researchers should focus on investigating the shared or common components across emotional disorders, particularly concerning individuals presenting with higher rates of suicidal ideation dysfunctional attitudes, and help-seeking behavior. Using higher-order dimensions of psychopathology could simplify the complex presentation of multiple co-occurring disorders and suggest valid constructs for future investigations.

Elevated depression and anxiety predict future patterns of individualistic and collectivistic cultural values: A cross-lagged longitudinal network analysis.

BACKGROUND: Specific components of independent and interdependent self-construal have been associated with psychopathology. However, most studies on this topic have been cross-sectional, precluding causal inferences. We used contemporaneous and temporal cross-lagged network analysis to establish weak causal effects in understanding the association between self-construal and psychopathology components. METHODS: Middle-aged and older community-dwelling adults (n = 3294) participated in the Midlife Development in the United States study across two time-points, spaced nine years apart. Six self-construal (interdependence: connection to others, commitment to others, receptiveness to influence; independence: behavioral consistency, sense of difference from others, self-reliance) and three psychopathology nodes (major depressive disorder (MDD), generalized anxiety disorder (GAD), and panic disorder (PD) symptom severity) were examined. All network analyses controlled for age, sex, race, and number of chronic illnesses as covariates. RESULTS: Contemporaneous and temporal networks yielded relations between elevated MDD and PD and increased receptiveness to influence. Heightened GAD symptom severity was associated with future increased difference from others and decreased connection to others, commitment to others, and receptiveness to influence. Higher MDD, GAD, and PD severity were associated with future lower self-reliance. Network comparison tests revealed no consistent network differences across sex and race. LIMITATIONS: DSM-III-R measures of MDD, GAD, and PD were used. Results may not generalize to culturally diverse racial groups. CONCLUSIONS: Changes in self-construal may result from increased MDD, GAD, and PD severity. Findings suggest the importance of targeting common mental health symptoms to positively influence how individuals view the self and others in various social contexts.

Training, supervision, and experience of coaches offering digital guided self-help for mental health concerns.

Accessible, low-cost intervention options are necessary to address the rise in mental health problems among college students. Digital guided self-help, or coached, programs have been developed to provide such services, with many commercially available. As such, there are a large and growing number of individuals coaching these programs. However, an unmet need is to evaluate and assess best practices for training and supervising individuals in these positions. To this end, we describe how we recruited, trained, and supervised coaches as part of a large randomized controlled trial using a widely available digital commercial platform. Coaches were trained to provide digital guided self-help for depression, anxiety, and/or eating disorders for college students. Coaches initially attended three live training sessions over 2-3 weeks, viewed multiple training videos, and read a detailed coaching manual developed by our team. Thereafter, they attended weekly supervision. Following their term, coaches completed an exit survey to assess their supervision and training experiences. A total of 37 of 70 (53%) graduate-level student coaches completed the survey. The experience was reported as very positive (95%). In particular, the majority reported feeling well prepared, more confident, and felt they had developed useful skills for their own practice.

Using Social Influence Strategies to Improve Rates of Online Mental Health Survey Participation: Results from Two Experiments.

Online surveys are routinely used in mental health screening and treatment follow-up assessment, though they can yield low response rates. We tested the effects of social psychology-informed influence strategies for increasing rates of participation in an online mental health screening survey (Experiment 1) and a treatment follow-up survey (Experiment 2). In Experiment 1 (N = 45,569), embedding one or any combination of three motivational appeals (personal gain, community gain, and inclusivity) in screening survey invitation and reminder emails unexpectedly led to lower rates of survey participation compared to when the appeals were not included (overall participation rate = 12.02%, ORs = 0.75 to 0.97, ps < .001). In Experiment 2 (N = 873), a video of a TikTok influencer encouraging survey participation embedded in treatment follow-up survey invitation and reminder emails did not significantly affect survey completion compared to a humorous gif unrelated to survey participation (overall participation rate = 47.88%, OR = 1.18, p = .200). Moderator analyses revealed that the video led to higher rates of participation than the gif among White participants (OR = 1.39, p = .031) and non-Hispanic participants (OR = 1.35, p = .029) only, whereas the video led to lower rates of participation than the gif among students who did not disclose their race (OR = 0.31, p = .010). Results suggested that efforts to improve online survey participation should be balanced with possible downsides (e.g., added email length) and should be evaluated for differential performance among population subgroups prior to widespread implementation.

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Intelligence on Society.

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion," coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

Preferred treatment focus among college students with eating disorders and comorbid mental health problems in a digital cognitive-behavioral guided self-help program.

OBJECTIVE: To examine the mental health problems that college students with eating disorders (EDs) and comorbid depression and/or anxiety disorders preferred to target first in a digital treatment program and explore correlates of preferred treatment focus. METHODS: Four hundred and eighty nine college student users of a digital cognitive-behavioral guided self-help program targeting common mental health problems (76.7% female, Mage = 20.4 ± 4.4, 64.8% White) screened positive for an ED and ≥one other clinical mental health problem (i.e., depression, generalized anxiety disorder, social phobia, and/or panic disorder). Students also reported on insomnia, post-traumatic stress, alcohol use, and suicide risk. Before treatment, they indicated the mental health problem that they preferred to target first in treatment. Preferred treatment focus was characterized by diagnostic profile (i.e., ED + Depression, ED + Anxiety, ED + Depression + Anxiety), symptom severity, and demographics. RESULTS: 58% of students with ED + Anxiety, 47% of those with ED + Depression, and 27% of those with ED + Depression + Anxiety chose to target EDs first. Across diagnostic profiles, those who chose to target EDs first had more severe ED symptoms than those who chose to target anxiety or depression (ps < .05). Among students with ED + Depression + Anxiety, those who chose to target EDs first had lower depression symptoms than those who chose to target depression, lower generalized anxiety than those who chose to target anxiety, and lower suicidality than those who chose to target anxiety or depression (ps < .01). CONCLUSIONS: Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles. Research should explore specific symptom presentations associated with preferred treatment focus. PUBLIC SIGNIFICANCE: Findings indicate that a sizable percentage of college students with depression/anxiety who also have EDs prefer to target EDs first in treatment, highlighting the importance of increasing availability of ED interventions to college students. Students with EDs and comorbid depression and/or anxiety disorders showed variable preferred treatment focus across diagnostic profiles, and preference to target EDs was associated with greater ED psychopathology across diagnostic profiles.

Reach and uptake of digital mental health interventions based on cognitive-behavioral therapy for college students: A systematic review.

Background: Mental health problems are increasing in prevalence among college students, yet few students receive treatment due to barriers such as insufficient resources in college counseling centers. Digital mental health interventions (DMHIs) have potential to overcome barriers and offer accessible, evidence-based care to college students. However, to evaluate the true public health impact of evidence-based DMHIs, it is important to assess the reach and uptake rates of DMHIs on college campuses. Objectives: We conducted a systematic review to examine the reach (i.e., % of invited students who express interest) and uptake (i.e., % of enrolled participants who initiate an intervention) of DMHIs based on cognitive-behavioral therapy (CBT) for college students. Methods: Eight databases were searched. Inclusion criteria included: (1) college population; (2) experimental design; (3) CBT-based intervention; (4) intervention targeting specific mental health conditions; and (5) digital intervention. Reach and uptake rates were calculated from data reported. A systematic narrative review framework was used to synthesize results. Results: Of 10,315 articles screened, 90 were included. Seventeen studies (19%) reported sufficient data to calculate reach; 35 studies (39%) reported uptake rates. Of studies that reported reach or uptake, most evaluated unguided (n = 20) or guided (n = 16) self-help programs. Measurement methods varied widely. Overall reach was low, whereas uptake was high among enrolled participants. Discussion: Despite evidence that improving reach and uptake can increase the public health impact of DMHIs, most studies did not report on either outcome. Suggested practices to improve these outcomes, and their reporting, are discussed.

Mindfulness enhances cognitive functioning: a meta-analysis of 111 randomized controlled trials.

BACKGROUND: Currently no comprehensive meta-analysis of MBI efficacy on global and unique cognitive subdomains exist. METHOD: Examined the effects of MBIs on global cognition and 15 cognitive subdomains. Inclusion criteria: meditation naïve participants; randomized controlled trial; outcome included one objective or subjective cognitive functioning measure; primary focus was teaching mindfulness skills. Exclusion criteria: inadequate data; one-session ; control condition contained any MBI component. Robust variance estimation and moderator analyses controlling for presence of treatment fidelity were conducted. RESULTS: One-hundred-and-eleven RCTs (n = 9,538) met eligibility criteria. MBIs had small-to-moderate significant effects on global cognition, executive attention, working memory accuracy, inhibition accuracy, shifting accuracy, sustained attention, and subjective cognitive functioning (vs. waitlist/no-treatment, g = 0.257-0.643; vs. active controls, g = 0.192-0.394). MBIs did not impact executive functioning (EF) latency indices, verbal fluency, processing speed, episodic memory, and cognitive error. Treatment effects were stronger for those with elevated psychiatric symptoms vs. healthy controls, and medical samples, studies with complete-case (vs. intention-to-treat) analysis, face-to-face (vs. self-guided) delivery, and non-standard (vs. standard MBI). CONCLUSION: MBIs consistently yielded small-to-moderate yet practically meaningful effect sizes on global cognition and six cognitive subdomains that captured accuracy vs. latency-based indices of EF and sustained accuracy.

Brief imaginal exposure exercises for social anxiety disorder: A randomized controlled trial of a self-help momentary intervention app.

INTRODUCTION: Emerging technological interventions for psychological disorders are being developed continually. Offering imaginal exposure exercises as a self-help intervention presents the opportunity to acquire foundational skills to address social anxiety. The current study evaluates the feasibility and effectiveness of a novel smartphone application for social interaction anxiety. METHODS: Participants (n = 82) were adults meeting criteria for social anxiety disorder. They were randomly assigned to imaginal exposure (IE; n = 39) or self-monitoring (n = 43) delivered multiple times daily via a smartphone application for a one-week trial. It was expected that participants using the IE exercises would demonstrate significantly greater declines in social anxiety in addition to increases in self-efficacy and that compliance would serve as a predictor of outcome. Mixed-effects models were utilized. RESULTS: Participants using IE (vs. self-monitoring) evidenced significantly greater reductions in social anxiety from pre- to post-treatment and at 1-month follow-up. Similarly, IE (vs. self-monitoring) led to significantly greater increases in self-efficacy from pre- to post-treatment and 1-month follow-up. Further, more completed IE exercises predicted significantly greater changes in social anxiety and self-efficacy at subsequent timepoints compared with self-monitoring. CONCLUSIONS: Findings suggest that a brief IE self-help intervention was effective in targeting social interaction anxiety.

Psychotherapy utilization by United States college students.

Objective: We studied current psychotherapy utilization rates among college students with mental health problems and identified characteristics associated with differential utilization. Participants: Nationwide online survey of students screening positive for at least one clinical mental health problem (N = 18,435). Methods: Rates and correlates of psychotherapy utilization were analyzed descriptively and with logistic regression. Results: Sample-wide, 19% reported receiving psychotherapy. Being male (vs. female), being Asian, Black or African American, or Multiracial (vs. White), greater financial difficulty, lower parent education, lower year in school, and attending a public (vs. private) institution were associated with lower utilization. Holding a gender minority (vs. female) identity and holding a sexual minority (vs. heterosexual) identity were associated with higher utilization. Utilization fell from Fall 2019 to Spring 2020, early during the COVID-19 pandemic, and subsequently rebounded. Conclusions: This study estimates current psychotherapy utilization among students with mental health problems and identifies possibly underserved populations.