Treatment as a moderator and executive function as a mediator of the effect of a mindfulness ecological momentary intervention for generalized anxiety disorder.

BACKGROUND: Theory and research indicated that executive functioning (EF) correlated with, preceded, and stemmed from worry in generalized anxiety disorder (GAD). The present secondary analysis (Zainal & Newman, 2023b) thus determined whether EF domains mediated the effect of a 14-day (5 prompts/day) mindfulness ecological momentary intervention (MEMI) against a self-monitoring control (SM) for GAD. METHOD: Participants (N = 110) diagnosed with GAD completed self-reported (Attentional Control Scale, GAD Questionnaire, Perseverative Cognitions Questionnaire) and performance-based tests (Letter-Number Sequencing, Stroop, Trail Making Test-B, Verbal Fluency) at baseline, post-treatment, and one-month follow-up (1MFU). Causal mediation analyses determined if pre-post changes in EF domains preceded and mediated the effect of MEMI against SM on pre-1MFU changes in GAD severity and trait repetitive negative thinking (RNT). RESULTS: MEMI was more efficacious than SM in improving pre-post inhibition (ß = -2.075, 95% [-3.388, -0.762], p = .002), working memory (ß = 0.512, 95% [0.012, 1.011], p = .045), and set-shifting (ß = -2.916, 95% [-5.142, -0.691], p = .010) but not verbal fluency and attentional control. Within groups, MEMI but not SM produced improvements in all examined pre-post EF outcomes except attentional control. Only pre-post improvements in inhibition mediated the effect of MEMI against SM on pre-1MFU reductions in GAD severity (ß = -0.605, 95% [-1.357, -0.044], p = .030; proportion mediated = 7.1%) and trait RNT (ß = -0.024, 95% [-0.054, -0.001], p = .040; proportion mediated = 7.4%). These patterns remained after conducting sensitivity analyses with non-linear mediator-outcome relations. CONCLUSIONS: Optimizing MEMI for GAD might entail specifically boosting inhibition plausibly by augmenting it with dialectical behavioral therapy, encouraging high-intensity physical exercises, and targeting negative emotional contrast avoidance.

Effects of Chatbot Components to Facilitate Mental Health Services Use in Individuals With Eating Disorders Following Online Screening: An Optimization Randomized Controlled Trial.

OBJECTIVE: Few individuals with eating disorders (EDs) receive treatment. Innovations are needed to identify individuals with EDs and address care barriers. We developed a chatbot for promoting services uptake that could be paired with online screening. However, it is not yet known which components drive effects. This study estimated individual and combined contributions of four chatbot components on mental health services use (primary), chatbot helpfulness, and attitudes toward changing eating/shape/weight concerns ("change attitudes," with higher scores indicating greater importance/readiness). METHODS: Two hundred five individuals screening with an ED but not in treatment were randomized in an optimization randomized controlled trial to receive up to four chatbot components: psychoeducation, motivational interviewing, personalized service recommendations, and repeated administration (follow-up check-ins/reminders). Assessments were at baseline and 2, 6, and 14 weeks. RESULTS: Participants who received repeated administration were more likely to report mental health services use, with no significant effects of other components on services use. Repeated administration slowed the decline in change attitudes participants experienced over time. Participants who received motivational interviewing found the chatbot more helpful, but this component was also associated with larger declines in change attitudes. Participants who received personalized recommendations found the chatbot more helpful, and receiving this component on its own was associated with the most favorable change attitude time trend. Psychoeducation showed no effects. DISCUSSION: Results indicated important effects of components on outcomes; findings will be used to finalize decision making about the optimized intervention package. The chatbot shows high potential for addressing the treatment gap for EDs.

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.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students.

BACKGROUND: A better understanding of the relationships between 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 which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms 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. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD). 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 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes. CONCLUSION: These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression. TRIAL REGISTRATION: Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

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.

Prospective network analysis of proinflammatory proteins, lipid markers, and depression components in midlife community women.

BACKGROUND: Vulnerability theories propose that suboptimal levels of lipid markers and proinflammatory proteins predict future heightened depression. Scar models posit the reverse association. However, most studies that tested relationships between non-specific immune/endocrine markers and depression did not separate temporal inferences between people and within-person and how different immunometabolism markers related to unique depression symptoms. We thus used cross-lagged prospective network analyses (CLPN) to investigate this topic. METHODS: Community midlife women (n = 2224) completed the Center for Epidemiologic Studies-Depression scale and provided biomarker samples across five time-points spanning 9 years. CLPN identified significant relations (edges) among components (nodes) of depression (depressed mood, somatic symptoms, interpersonal issues), lipid markers [insulin, fasting glucose, triglycerides, low-density lipoprotein-cholesterol (LDL), high-density lipoprotein-cholesterol (HDL)], and proinflammatory proteins [C-reactive protein (CRP), fibrinogen], within and across time-points. All models adjusted for age, estradiol, follicle-stimulating hormone, and menopausal status. RESULTS: In within-person temporal networks, higher CRP and HDL predicted all three depression components (d = 0.131-2.112). Increased LDL preceded higher depressed mood and interpersonal issues (v. somatic symptoms) (d = 0.251-0.327). Elevated triglycerides predicted more somatic symptoms (v. depressed mood and interpersonal problems) (d = 0.131). More interpersonal problems forecasted elevated fibrinogen and LDL levels (d = 0.129-0.331), and stronger somatic symptoms preceded higher fibrinogen levels (d = 0.188). CONCLUSIONS: Results supported both vulnerability and scar models. Long-term dysregulated immunometabolism systems, social disengagement, and related patterns are possible mechanistic accounts. Cognitive-behavioral therapies that optimize nutrition and physical activity may effectively target depression.

A cross-lagged prospective network analysis of depression and anxiety and cognitive functioning components in midlife community adult women.

BACKGROUND: Scar theory proposes that heightened depression and anxiety precede and predict worse cognitive functioning outcomes, whereas the vulnerability theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged prospective network analyses to facilitate causal inferences in understanding the relations between psychopathology and cognitive functioning components. METHODS: Racially-diverse midlife women (n = 1816) participated in the Study of Women's Health Across the Nation at two time-points, spanning one year apart. Five psychopathology (anxiety severity, depressed mood, somatic symptoms, positive affect, interpersonal problems) and four cognitive functioning nodes (working memory (WM), processing speed (PS), facial recognition (FCR), and verbal memory (VRM)) were assessed. All analyses adjusted for age, menopausal status, estradiol, and follicle-stimulating hormones. RESULTS: Contemporaneous networks yielded notable inverse between-node relations (edges) between interpersonal problems and reduced FCR and PS, and between depressed mood and lower FCR, VRM, or PS. Nodes that had the highest likelihood to bridge other constructs were positive affect, anxiety severity, WM, and VRM. Temporal networks produced edges consistent with the scar (v. vulnerability) hypotheses. Higher somatic symptoms were related to reduced PS and WM, and greater depressed mood was correlated with lower future PS and WM. Also, higher anxiety severity coincided with decreased future PS and WM. Greater positive affect was associated with stronger future PS, FCR, and WM. Also, positive affect had the strongest relations with other nodes. CONCLUSIONS: Findings suggest the importance of targeting symptoms and cognitive functioning simultaneously.

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.

Baseline overly accommodating interpersonal problems in relation to parsed alliance-outcome associations in cognitive behavioral therapy for generalized anxiety disorder.

Given its interpersonal underpinnings, relational factors may be salient in psychotherapy for generalized anxiety disorder (GAD). Supporting this point, research has indicated a positive total alliance-improvement correlation in cognitive behavioral therapy (CBT) for GAD. However, less research has disaggregated this correlation into within- and between-patient components, or examined theory-informed ways in which patient characteristics influence to these components. Thus, we first investigated parsed alliance-outcome associations in CBT for GAD. Second, consistent with theory that alliance may represent a direct interpersonal change correlate, we tested whether within-patient alliance improvements were especially therapeutic for patients with higher levels of an interpersonal problem prototypical of GAD-over accommodation. Also, consistent with theory that between-patient differences in overall alliance may be influenced by patients' preexisting relational characteristics, we tested whether more overly accommodating patients reported poorer average alliances that, in turn, related to worse outcomes.Sixty-nine patients received variants of CBT. Patients rated over accommodation at baseline, and alliance and outcome across treatment.As hypothesized, within-patient alliance improvements correlated with subsequent anxiety reduction, and this association was stronger for more overly accommodating patients. All between-patient associations were nonsignificant.Results help clarify the nuanced role of alliance in CBT for GAD.

Executive Functioning Constructs in Anxiety, Obsessive-Compulsive, Post-Traumatic Stress, and Related Disorders.

PURPOSE OF REVIEW: We synthesize theories proposing complex relations between cognitive functioning and anxiety-related concepts. We evaluate vulnerability theories suggesting that deficits in various cognitive functioning domains predict future anxiety-associated concepts. We examine scar theories asserting the opposite direction of effects (i.e., anxiety predicting cognitive dysfunction). Furthermore, we examine more novel frameworks on this topic. RECENT FINDINGS: Reliable evidence exists for the scar and vulnerability theories. This includes mounting data on diverse anxiety symptoms predicting cognitive dysfunction (and conversely) unfolding at between- and within-person levels (dynamic mutualism theory). It also includes data on the stronger effects or central influence of anxiety (versus non-anxiety) symptoms on executive functioning (EF; i.e., higher-order cognitive control governing myriad thinking and action repertoires) versus non-EF domains and vice versa (network theory). In addition, it reviews emerging evidence that enhanced cognitive control can correlate with higher anxiety among children (overgeneralized control theory). The generally inverse relations between anxiety symptoms and cognitive dysfunction are bidirectional and complex within and between persons. Plausible mediators and moderators merit more attention, including immune, metabolism, and neural markers and the social determinants of health.

Emotion network density is a potential clinical marker for anxiety and depression: Comparison of ecological momentary assessment and daily diary.

OBJECTIVES: Using two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression. DESIGN: Mobile-based intensive longitudinal assessments. METHODS: 119 participants (61 anxious and depressed, 58 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set. RESULTS: In the EMA data set, both NE and PE network densities significantly predicted participants' diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status. CONCLUSIONS: Greater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders. PRACTITIONER POINTS: Emotion network density, or the degree to which prior emotions predict and influence current emotions, indicates an inflexible or change-resistant emotion system. Emotional inflexibility or change resistance over a few hours, but not daily, may characterize anxiety and depressive disorders. Inflexible negative emotion systems are associated with anxiety and depressive disorders, whereas inflexible positive emotion systems may indicate psychological health. Considering emotional inflexibility within days may provide additional information beyond demographics and mean level and variability of emotions in daily life for detecting anxiety and depressive disorders.

Depression and worry symptoms predict future executive functioning impairment via inflammation.

BACKGROUND: Scar models posit that heightened anxiety and depression can increase the risk for subsequent reduced executive function (EF) through increased inflammation across months. However, the majority of past research on this subject used cross-sectional designs. We therefore examined if elevated generalized anxiety disorder (GAD), major depressive disorder (MDD), and panic disorder (PD) symptoms forecasted lower EF after 20 months through heightened inflammation. METHODS: Community-dwelling adults partook in this study (n = 614; MAGE = 51.80 years, 50% females). Time 1 (T1) symptom severity (Composite International Diagnostic Interview - Short Form), T2 (2 months after T1) inflammation serum levels (C-reactive protein, fibrinogen, interleukin-6), and T3 (20 months after T1) EF (Brief Test of Adult Cognition by Telephone) were assessed. Structural equation mediation modeling was performed. RESULTS: Greater T1 MDD and GAD (but not PD) severity predicted increased T2 inflammation (Cohen's d = 0.21-1.92). Moreover, heightened T2 inflammation forecasted lower T3 EF (d = -1.98 to -1.87). T2 inflammation explained 25-32% of the negative relations between T1 MDD or GAD and T3 EF. T1 GAD severity predicting T3 EF via T2 inflammation path was stronger among younger (v. older) adults. Direct effects of T1 MDD, GAD, and PD forecasting decreased T3 EF were found (d = -2.02 to -1.92). Results remained when controlling for socio-demographic, physical health, and lifestyle factors. CONCLUSIONS: Inflammation can function as a mechanism of the T1 MDD or GAD-T3 EF associations. Interventions that successfully treat depression, anxiety, and inflammation-linked disorders may avert EF decrements.

Within-person increase in pathological worry predicts future depletion of unique executive functioning domains.

BACKGROUND: Affective neuroscience and scar theories propose that increased excessive worry, the hallmark symptom of generalized anxiety disorder (GAD), predicts future declines in executive functioning (EF). However, the preponderance of cross-sectional designs used to examine between-person chronic worry-EF relationships has blocked progress on understanding their potentially causal within-person associations. Accordingly, this study used bivariate dual latent change score (LCS) models to test whether within-person increased GAD severity might relate to future reduced EF. METHODS: Community-dwelling adults (N = 2581, 46 years on average, s.d. = 11.40, 54.71% female) were assessed for GAD symptom severity (Composite International Diagnostic Interview-Short Form) across three waves, spaced about 9 years apart. Three aspects of EF [inhibition, set-shifting, and mixing costs (MCs; a measure related to common EF)], were assessed with stop-and-go switch tasks. Participants responded to 20 normal and 20 reverse single-task block trials and 32 mixed-task switch block trials. EF tests were administered at time 2 (T2) and time 3 (T3), but not at time 1 (T1). RESULTS: After controlling for T1 depression, LCS models revealed that within-person increased T1 - T2 GAD severity substantially predicted future reduced T2 - T3 inhibition and set-shifting (both indexed by accuracy and latency), and MC (indexed by latency) with moderate-to-large effect sizes (|d| = 0.51-0.96). CONCLUSIONS: Results largely support scar theories by offering preliminary within-person, naturalistic evidence that heightened excessive worry can negatively predict future distinct aspects of cognitive flexibility. Effectively targeting pathological worry might prevent difficulties arising from executive dysfunction.

Larger increase in trait negative affect is associated with greater future cognitive decline and vice versa across 23 years.

BACKGROUND: Trait negative affect (NA) is a central feature of anxiety and depression disorders. Neurocognitive and scar models propose that within-person increase in NA across one period of time relates to a decline in cognitive functioning at a future period of time and vice versa. Yet, there has been little research on whether a within-person change in trait NA across one time-lag precedes and is associated with a change in cognition across a future time lag and vice versa. Due to a growing aging population, such knowledge can inform evidence-based prevention. METHODS: Participants were 520 dementia-free community-dwelling adults (mean age = 59.76 years [standard deviation = 8.96], 58.08% females). Trait-level NA (negative emotionality scale), spatial cognition (block design and card rotations), verbal working memory (WM; digit span backward), and processing speed (symbol digit modalities) were assessed at five time points (waves) across 23 years. Bivariate dual latent change score (LCS) approaches were used to adjust for regression to the mean, lagged outcomes, and between-person variability. RESULTS: Unique bivariate LCS models showed that within-person increase in trait NA across two sequential waves was related to declines in spatial cognition, verbal WM, and processing speed across the subsequent two waves. Moreover, within-person reductions in spatial cognition, verbal WM, and processing speed across two sequential waves were associated with future increases in trait NA across the subsequent two waves. CONCLUSIONS: Findings concur with neurobiological and scar theories of psychopathology. Furthermore, results support process-based emotion regulation models that highlight the importance of verbal WM, spatial cognition, and processing speed capacities for downregulating NA.

A randomized controlled trial of a smartphone-based application for the treatment of anxiety.

AbstractIntroduction: Generalized anxiety disorder (GAD) is prevalent among college students. Smartphone-based interventions may be a low-cost treatment method. Method: College students with self-reported GAD were randomized to receive smartphone-based guided self-help (n = 50), or no treatment (n = 50). Post-treatment and six-month follow-up outcomes included the Depression Anxiety Stress Scales-Short Form Stress Subscale (DASS Stress), the Penn State Worry Questionnaire (PSWQ-11), and the State-Trait Anxiety Inventory-Trait (STAI-T), as well as diagnostic status assessed by the GAD-Questionnaire, 4th edition. Results: From pre- to post-treatment, participants who received guided self-help (vs. no treatment) experienced significantly greater reductions on the DASS Stress (d = -0.408) and a greater probability of remission from GAD (d = -0.445). There was no significant between-group difference in change on the PSWQ-11 (d = -0.208) or STAI-T (d = -0.114). From post to six-month follow-up there was no significant loss of gains on DASS Stress scores (d = -0.141) and of those who had remitted, 78.6% remained remitted. Yet rates of remitted participants no longer differed significantly between conditions at follow-up (d = -0.229). Conclusion: Smartphone-based interventions may be efficacious in treating some aspects of GAD. Methods for improving symptom reduction and long-term outcome are discussed.

ARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild.

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9876 video clips of body movements and 13,239 human characters, named Body Language Dataset (BoLD), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named Automated Recognition of Bodily Expression of Emotion (ARBEE), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans.

Examining the initial usability, acceptability and feasibility of a digital mental health intervention for college students in India.

Generalised anxiety disorder (GAD) is prevalent among college students in India; however, barriers like stigma, treatment accessibility and cost prevent engagement in treatment. Web- and mobile-based, or digital, mental health interventions have been proposed as a potential solution to increasing treatment access. With the ultimate goal of developing an engaging digital mental health intervention for university students in India, the current study sought to understand students' reactions to a culturally and digitally adapted evidence-based cognitive behavioural therapy (CBT) for GAD intervention. Specifically, through theatre testing and focus groups with a non-clinical sample of 15 college students in India, the present study examined initial usability, acceptability and feasibility of the "Mana Maali Digital Anxiety Program." Secondary objectives comprised identifying students' perceived barriers to using the program and eliciting recommendations. Results indicated high usability, with the average usability rating ranking in the top 10% of general usability scores. Participants offered actionable changes to improve usability and perceived acceptability among peers struggling with mental health issues. Findings highlight the benefits of offering digital resources that circumvent barriers associated with accessing traditional services. Results build on existing evidence that digital interventions can be a viable means of delivering mental healthcare to large, defined populations.

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data.

With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.