Predicting response to a smartphone-based cognitive-behavioral therapy for body dysmorphic disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a severe, chronic disorder if untreated. Smartphone cognitive behavioral therapy (CBT) for BDD is efficacious and can reduce key treatment barriers (e.g., lack of clinicians, cost, stigma). While promising, little is known about who is more or less likely to benefit from this approach. METHODS: This is a secondary data analysis of a randomized, waitlist-controlled trial of smartphone CBT for BDD. Participants (N = 80) were recruited nationally and randomized to receive a 12-week, coach-guided CBT for BDD app, either immediately or after a 12-week waitlist. The main outcome for this analysis was BDD severity (BDD-YBOCS) over time (baseline, week 6, week 12) during the active app use phase in each randomized group (n = 74). Secondary outcomes included treatment response (≥30 % reduction in BDD-YBOCS) and remission (total BDD-YBOCS ≤16) at end-of-treatment. RESULTS: Immediate (vs. delayed) CBT predicted better outcomes (symptom improvement), as did gender identity (symptom improvement), higher baseline treatment credibility and expectancy (response, remission), lower baseline BDD severity (remission), and sexual minority status (vs. heterosexual; response, remission). LIMITATIONS: Limitations include the relatively small sample, drop-out rate of 22 %, and limited gender and racial-ethnic diversity. CONCLUSIONS: These results highlight a potential advantage of smartphone CBT in historically marginalized populations, and the importance of efforts to hasten treatment access, bolster confidence in the treatment at treatment onset, and develop stratified care models to optimize treatment allocation and efficacy.

Investigating psychiatric symptoms as predictors of the reasons people do not regulate their emotions in daily life.

Investigations into emotion dysregulation predominantly focus on ineffective strategy selection and implementation. However, little empirical attention has been given to the possibility that failure to engage in emotion regulation (ER) may also indicate emotion dysregulation, especially when the reason for not regulating suggests skill or motivational deficits. We randomly sampled ER strategy use up to six times per day for 5 weeks in 113 adults with elevated social anxiety. When participants reported not engaging in any recent ER, they indicated their reason for not regulating. Data were collected between 2018 and 2019. Participants reported not regulating in 60.9% of surveys. The most frequently endorsed reason for not regulating was "I was not experiencing any thoughts/feelings that I wanted to change," followed by "I did not want to put in the effort," then "I did not know what to do," and finally, "I did not think I was capable." After controlling for within- and between-person state affect, participants with lower trait-level social anxiety, depression, and emotion dysregulation scores were more likely to report no ER because they were not experiencing any thoughts/feelings that they wanted to change relative to the other three reasons that might reflect barriers to effective regulation. Trait-level generalized worry and alcohol use did not significantly predict between nonregulation reasons. Findings from these preregistered analyses suggest that not regulating-especially among persons higher in social anxiety, depression, and emotion dysregulation-may indicate ER skill or motivation deficits that point to opportunities for intervention. Results may not generalize to more demographically or clinically diverse samples. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

How does emotion regulation change during psychotherapy? A daily diary study of adults in a transdiagnostic partial hospitalization program.

OBJECTIVE: Improvement in emotion regulation is a proposed transdiagnostic mechanism of change. However, treatment research is limited by disorder-specific investigations that assess a narrow number of emotion regulation strategies. Moreover, most assess pre-to-post-treatment change without examining short-term changes throughout psychotherapy that might influence treatment response. METHOD: To address these gaps, this study uses daily diary methodology to examine trajectories of change in use of six emotion regulation strategies during partial hospitalization psychiatric treatment. Treatment was rooted in cognitive behavioral principles and included skills adapted from empirically supported cognitive behavioral therapy (CBT) and dialectical behavior therapy (DBT) manuals. Participants were adults (N = 364; Mage = 34.6 years; 60% female; 85% non-Hispanic White) with various profiles of mood, anxiety, and obsessive-compulsive disorders who completed symptom measures at baseline and discharge and daily measures of emotion regulation. RESULTS: In the first 7 treatment days, patients increased use of engagement strategies (reappraisal, acceptance) and decreased use of disengagement (expressive suppression) and cognitive perseveration (experiential avoidance, rumination) strategies. Day-to-day trajectories found that decreased use of experiential avoidance predicted next-day changes in distraction and suppression use. In predicting treatment outcomes, steeper rates of decreased suppression use predicted reductions in anxiety, depression, and general psychopathology symptoms; similar patterns were observed for decreased rumination and experiential avoidance use and increased reappraisal use. CONCLUSION: Results add to a growing literature on the value of intentional, constructive engagement with emotional experiences as a mechanism of psychological health. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

State and Trait Emotion Regulation Diversity in Social Anxiety.

Emotion regulation (ER) diversity, defined as the variety, frequency, and evenness of ER strategies used, may predict social anxiety (SA) severity. In a sample of individuals with high (n=113) or low (n=42) SA severity, we tested whether four trait ER diversity metrics predicted group membership. We generalized existing trait ER diversity calculations to repeated-measures data to test if state-level metrics (using two weeks of EMA data) predicted SA severity within the higher severity group. As hypothesized (osf.io/xadyp), higher trait ER diversity within avoidance-oriented strategies predicted greater likelihood of belonging to the higher severity group. At the state-level, higher diversity across all ER strategies, and within and between avoidance- and approach-oriented strategies, predicted higher SA severity (but only after controlling for number of submitted EMAs). Only diversity within avoidance-oriented strategies was significantly correlated across trait and state levels. Findings suggest that high avoidance-oriented ER diversity may co-occur with higher SA severity.

Separating Long-Term Equilibrium Adaptation from Short-Term Self-Regulation Dynamics Using Latent Differential Equations.

Self-regulating systems change along different timescales. Within a given week, a depressed person's affect might oscillate around a low equilibrium point. However, when the timeframe is expanded to capture the year during which they onboarded antidepressant medication, their equilibrium and oscillatory patterns might reorganize around a higher affective point. To simultaneously account for the meaningful change processes that happen at different time scales in complex self-regulatory systems, we propose a single model that combines a second-order linear differential equation for short timescale regulation and a first-order linear differential equation for long timescale adaptation of equilibrium. This model allows for individual-level moderation of short-timescale model parameters. The model is tested in a simulation study which shows that, surprisingly, the short and long timescales can fully overlap and the model still converges to the reasonable estimates. Finally, an application of this model to self-regulation of emotional well-being in recent widows is presented and discussed.

Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms.

BACKGROUND: Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristic of social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement learning in individuals who are anxious. OBJECTIVE: This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 types of social reinforcement learning relevant to social anxiety: learning about other people and learning about one's own social performance. METHODS: Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people in both laboratory sessions and a task that assessed social reinforcement learning about one's own social performance in the second session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed effects models. RESULTS: After the CBM-I intervention, participants updated their beliefs about others more slowly (P=.04; Cohen d=-0.29) but used what they learned to make more accurate decisions (P=.005; Cohen d=0.20), choosing rewarding faces more frequently. These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their expectancies about others more from rewarding (P=.003; Cohen d=0.43) and less from punishing outcomes (P=.001; Cohen d=-0.47), and they became more accurate at learning to avoid punishing faces (P=.001; Cohen d=0.20). CONCLUSIONS: Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious, although replication will be important.

Associations between context and affect within the daily lives of cancer caregivers.

PURPOSE: This study used ecological momentary assessment (EMA) to test the association between activity, location, and social company contexts with cancer caregivers' in-the-moment affect to identify precisely when and where to deliver psychological interventions for caregivers. METHODS: Current cancer caregivers (N = 25) received 8 EMA prompts per day for 7 consecutive days. At each prompt, caregivers reported their current positive affect and negative affect, as well as what they were doing, where they were located, and who they were with. Multilevel logistic regressions tested the associations between caregivers' contexts with their own person-mean-centered state (concurrent momentary level) and trait (overall weekly average) positive or negative affect. RESULTS: Caregivers reported lower state negative affect, as well as higher state positive affect, when socializing (ps < .001), when at a public location (ps < .03), and when around their friends, family, spouse/partner, or care recipient (i.e., person with cancer, ps < .02), relative to when not endorsing the context. Caregivers also reported lower state negative affect when eating/drinking or engaging in leisure (ps < .01; but no parallel effects for state positive affect). Caregivers reported higher state negative affect while working, when at their workplace, or when around work colleagues (ps < .001) and lower state positive affect when at home or alone (ps < .03). CONCLUSIONS: Results suggest the pertinence of a behavioral activation framework to mitigate the emotional strain of caregiving. Interventions that facilitate caregivers' ability to socialize with a range of friends and family, including their loved one with cancer, outside of the home may have the strongest positive emotional impact.

Online sleep diaries: considerations for system development and recommendations for data management.

STUDY OBJECTIVES: To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management procedures to address common data entry errors that occur despite those considerations. METHODS: The online sleep diary capture component of the Sleep Healthy Using the Internet (SHUTi) intervention has been designed to promote data integrity. Features include diary entry restrictions to limit retrospective bias, reminder prompts and data visualizations to support user engagement, and data validation checks to reduce data entry errors. Despite these features, data entry errors still occur. Data management procedures relying largely on programming syntax to minimize researcher effort and maximize reliability and replicability. Presumed data entry errors are identified where users are believed to have incorrectly selected a date or AM versus PM on the 12-hour clock. Following these corrections, diaries are identified that have unresolvable errors, like negative total sleep time. RESULTS: Using the example of one of our fully-powered, U.S. national SHUTi randomized controlled trials, we demonstrate the application of these procedures: of 45,598 total submitted diaries, 487 diaries (0.01%) required modification due to date and/or AM/PM errors and 27 diaries (<0.001%) were eliminated due to unresolvable errors. CONCLUSION: To secure the most complete and valid data from online sleep diary systems, it is critical to consider the design of the data collection system and to develop replicable processes to manage data. CLINICAL TRIAL REGISTRATION: Sleep Healthy Using The Internet for Older Adult Sufferers of Insomnia and Sleeplessness (SHUTiOASIS); https://clinicaltrials.gov/ct2/show/NCT03213132; ClinicalTrials.gov ID: NCT03213132.

Assessing Emotion Polyregulation in Daily Life: Who Uses It, When Is It Used, and How Effective Is It?

Most research on emotion regulation has focused on understanding individual emotion regulation strategies. Preliminary research, however, suggests that people often use several strategies to regulate their emotions in a given emotional scenario (polyregulation). The present research examined who uses polyregulation, when polyregulation is used, and how effective polyregulation is when it is used. College students (N = 128; 65.6% female; 54.7% White) completed an in-person lab visit followed by a 2-week ecological momentary assessment protocol with six randomly timed survey prompts per day for up 2 weeks. At baseline, participants completed measures assessing past-week depression symptoms, social anxiety-related traits, and trait emotion dysregulation. During each randomly timed prompt, participants reported up to eight strategies used to change their thoughts or feelings, negative and positive affect, motivation to change emotions, their social context, and how well they felt they were managing their emotions. In pre-registered analyses examining the 1,423 survey responses collected, polyregulation was more likely when participants were feeling more intensely negative and when their motivation to change their emotions was stronger. Neither sex, psychopathology-related symptoms and traits, social context, nor subjective effectiveness was associated with polyregulation, and state affect did not moderate these associations. This study helps address a key gap in the literature by assessing emotion polyregulation in daily life. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00166-x.

The differential impact of COVID-19 across health service psychology students of color: An embedded mixed-methods study.

OBJECTIVES: Communities of color in the United States systematically experience inequities in physical and mental health care compared to individuals who identify as non-Hispanic White. The coronavirus disease 2019 (COVID-19) pandemic exacerbated these structural drivers of inequity to disproportionate and devastating effects for persons of color. In addition to managing the direct effects of COVID-19 risk, persons of color were also navigating increased racial prejudice and discrimination. For mental health professionals and trainees of color, the effects of COVID-19 racial health disparities and the increase in acts of racism may have been compounded by their work responsibilities. The current study used an embedded mixed-methods approach to examine the differential impact of COVID-19 on health service psychology (HSP) students of color as compared to their non-Hispanic White peers. METHOD: Using quantitative and qualitative data from the Epidemic-Pandemic Impacts Inventory, measures of perceived support and of discrimination, and open-ended questions about students' experiences with racism and microaggressions, we examined the extent to which different racial/ethnic HSP student groups experienced COVID-19-related discrimination, the impacts of COVID-19 felt by students of color, and how these experiences differed from those of their non-Hispanic White peers. RESULTS: HSP students of color endorsed greater impacts of the pandemic on both self and others in the home, perceived themselves as less supported by others, and reported more experiences of racial discrimination than non-Hispanic White HSP students. CONCLUSION: Throughout the graduate experience, HSP students of color and their experiences of discrimination need to be addressed. We provided recommendations to HSP training program directors and students both during and after the COVID-19 pandemic.

Examining social reinforcement learning in social anxiety.

BACKGROUND AND OBJECTIVES: Reinforcement learning biases have been empirically linked to anhedonia in depression and theoretically linked to social anhedonia in social anxiety disorder, but little work has directly assessed how socially anxious individuals learn from social reward and punishment. METHODS: N = 157 individuals high and low in social anxiety symptoms completed a social probabilistic selection task that involved selecting between pairs of neutral faces with varying probabilities of changing to a happy or angry face. Computational modeling was performed to estimate learning rates. Accuracy in choosing the more rewarding face was also analyzed. RESULTS: No significant group differences were found for learning rates. Contrary to hypotheses, participants high in social anxiety showed impaired punishment learning accuracy; they were more accurate at choosing the most rewarding face than they were at avoiding the most punishing face, and their punishment learning accuracy was lower than that of participants low in social anxiety. Secondary analyses found that high (vs. low) social anxiety participants were less accurate at selecting the more rewarding face on more (vs. less) punishing face pairs. LIMITATIONS: Stimuli were static, White, facial images, which lack important social cues (e.g., movement, sound) and diversity, and participants were largely non-Hispanic, White undergraduates, whose social reinforcement learning may differ from individuals at different developmental stages and those holding more marginalized identities. CONCLUSIONS: Socially anxious individuals may be less accurate at learning to avoid social punishment, which may maintain negative beliefs through an interpersonal stress generation process.

Integrated model of primary and mental healthcare for the refugee population served by an academic medical centre.

Refugees are at increased risk for developing mental health concerns due to high rates of trauma exposure and postmigration stressors. Moreover, barriers to accessing mental health services result in ongoing suffering within this population. Integrated care-which combines primary healthcare and mental healthcare into one cohesive, collaborative setting-may improve refugees' access to comprehensive physical and mental health services to ultimately better support this uniquely vulnerable population. Although integrated care models can increase access to care by colocating multidisciplinary services, establishing an effective integrated care model brings unique logistic (eg, managing office space, delineating roles between multiple providers, establishing open communication practices between specialty roles) and financial (eg, coordinating across department-specific billing procedures) challenges. We therefore describe the model of integrated primary and mental healthcare used in the International Family Medicine Clinic at the University of Virginia, which includes family medicine providers, behavioural health specialists and psychiatrists. Further, based on our 20-year history of providing these integrated services to refugees within an academic medical centre, we offer potential solutions for addressing common challenges (eg, granting specialty providers necessary privileges to access visit notes entered by other specialty providers, creating a culture where communication between providers is the norm, establishing a standard that all providers ought to be CC'ed on most visit notes). We hope that our model and the lessons we have learned along the way can help other institutions that are interested in developing similar integrated care systems to support refugees' mental and physical health.

Stress generation in social anxiety and depression: A two-study community assessment.

BACKGROUND: Stress generation theory suggests that people engage in certain behaviors that causally generate "dependent" stressful life events. Stress generation has primarily been studied in the context of depression with limited consideration of anxiety. People with social anxiety exhibit maladaptive social and regulatory behaviors that may uniquely generate stress. METHOD: Across two studies, we examined if people with elevated social anxiety experienced more dependent stressful life events than those lower in social anxiety. On an exploratory basis, we examined differences in perceived intensity, chronicity, and self-blame of stressful life events. As a conservative test, we examined whether observed relationships held after covarying depression symptoms. Community adults (Ns = 303; 87) completed semi-structured interviews about recent stressful life events. RESULTS: Participants with higher social anxiety symptoms (Study 1) and social anxiety disorder (SAD; Study 2) reported more dependent stressful life events than those with lower social anxiety. In Study 2, healthy controls rated dependent events as less impactful than independent events; those with SAD rated dependent and independent events as equally impactful. Regardless of social anxiety symptoms, participants placed greater blame on themselves for the occurrence of dependent than independent events. LIMITATIONS: Life events interviews are retrospective and preclude conclusions about short-term changes. Mechanisms of stress generation were not assessed. CONCLUSIONS: Results provide initial evidence for the role of stress generation in social anxiety that may be distinct from depression. Implications for assessing and treating unique and shared features of affective disorders are discussed.

Stability and spread: A novel method for quantifying transitions within multivariate binary time series data.

We present a novel method for quantifying transitions within multivariate binary time series data, using a sliding series of transition matrices, to derive metrics of stability and spread. We define stability as the trace of a transition matrix divided by the sum of all observed elements within that matrix. We define spread as the number of all non-zero cells in a transition matrix divided by the number of all possible cells in that matrix. We developed this method to allow investigation into high-dimensional, sparse data matrices for which existing binary time series methods are not designed. Results from 1728 simulations varying six parameters suggest that unique information is captured by both metrics, and that stability and spread values have a moderate inverse association. Further, simulations suggest that this method can be reliably applied to time series with as few as nine observations per person, where at least five consecutive observations construct each overlapping transition matrix, and at least four time series variables compose each transition matrix. A pre-registered application of this method using 4 weeks of ecological momentary assessment data (N = 110) showed that stability and spread in the use of 20 emotion regulation strategies predict next timepoint affect after accounting for affect and anxiety's auto-regressive and cross-lagged effects. Stability, but not spread, also predicted next timepoint anxiety. This method shows promise for meaningfully quantifying two unique aspects of switching behavior in multivariate binary time series data.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).

Characterizing changes in mental health-related outcomes for health service psychology graduate students during the first year of the COVID-19 pandemic.

OBJECTIVES: Health service psychology (HSP) graduate students experienced adverse mental health outcomes during COVID-19. However, little is known about how mental health outcomes changed in this population after the onset of COVID-19. METHODS: N = 496 HSP graduate students reported onset or worsening of mental health outcomes, inability to access mental health care, worry about COVID-19, and stress at two different timepoints during the first year of the COVID-19 outbreak (timepoint 1: May 1 to June 25, 2020; timepoint 2: September 2 to October 17, 2020). This study tested whether mental health outcomes improved, worsened, or stayed stable during this timeframe. The study also examined whether rising COVID-19 case rates in the state where a participant lived moderated changes in mental health outcomes. RESULTS: Overall, HSP graduate students endorsed adverse mental health outcomes at a higher rate during the first survey relative to the second survey. Even still, 62.68% of students reported worsened mental health symptoms, 49.84% reported worsened sleep, and 23.92% reported increased alcohol and substance use in the 2 months leading up to the second survey. CONCLUSION: HSP programs should monitor graduate students' evolving mental health, provide wellness resources, and adopt flexible approaches to support graduate students navigating training during periods of immense disruption.

Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life.

BACKGROUND: Social anxiety disorder is associated with distinct mobility patterns (e.g., increased time spent at home compared to non-anxious individuals), but we know little about if these patterns change following interventions. The ubiquity of GPS-enabled smartphones offers new opportunities to assess the benefits of mental health interventions beyond self-reported data. OBJECTIVES: This pre-registered study (https://osf.io/em4vn/?view_only=b97da9ef22df41189f1302870fdc9dfe) assesses the impact of a brief, online cognitive training intervention for threat interpretations using passively-collected mobile sensing data. DESIGN: Ninety-eight participants scoring high on a measure of trait social anxiety completed five weeks of mobile phone monitoring, with 49 participants randomly assigned to receive the intervention halfway through the monitoring period. RESULTS: The brief intervention was not reliably associated with changes to participant mobility patterns. CONCLUSIONS: Despite the lack of significant findings, this paper offers a framework within which to test future intervention effects using GPS data. We present a template for combining clinical theory and empirical GPS findings to derive testable hypotheses, outline data processing steps, and provide human-readable data processing scripts to guide future research. This manuscript illustrates how data processing steps common in engineering can be harnessed to extend our understanding of the impact of mental health interventions in daily life.

Building an emotion regulation recommender algorithm for socially anxious individuals using contextual bandits.

OBJECTIVES: Poor emotion regulation (ER) has been implicated in many mental illnesses, including social anxiety disorder. To work towards a scalable, low-cost intervention for improving ER, we developed a novel contextual recommender algorithm for ER strategies. DESIGN: N = 114 socially anxious participants were prompted via a mobile app up to six times daily for five weeks to report their emotional state, use of 19 different ER strategies (or no strategy), physical location, and social context. Information from passive sensors was also collected. METHODS: Given the large number of ER strategies, we used two different approaches for variable reduction: (1) grouping ER strategies into categories based on a prior meta-analysis, and (2) considering only the ten most frequently used strategies. For each approach, an algorithm that recommends strategies based on one's current context was compared with an algorithm that recommends ER strategies randomly, an algorithm that always recommends cognitive reappraisal, and the person's observed ER strategy use. Contextual bandits were used to predict the effectiveness of the strategies recommended by each policy. RESULTS: When strategies were grouped into categories, the contextual algorithm was not the best performing policy. However, when the top ten strategies were considered individually, the contextual algorithm outperformed all other policies. CONCLUSIONS: Grouping strategies into categories may obscure differences in their contextual effectiveness. Further, using strategies tailored to context is more effective than using cognitive reappraisal indiscriminately across all contexts. Future directions include deploying the contextual recommender algorithm as part of a just-in-time intervention to assess real-world efficacy. PRACTITIONER POINTS: Emotion regulation strategies vary in their effectiveness across different contexts. An algorithm that recommends emotion regulation strategies based on a person's current context may one day be used as an adjunct to treatment to help dysregulated individuals optimize their in-the-moment emotion regulation. Recommending flexible use of emotion regulation strategies across different contexts may be more effective than recommending cognitive reappraisal indiscriminately across all contexts.

Tangle: A metric for quantifying complexity and erratic behavior in short time series.

Temporal complexity refers to qualities of a time series that are emergent, erratic, or not easily described by linear processes. Quantifying temporal complexity within a system is key to understanding the time based dynamics of said system. However, many current methods of complexity quantification are not widely used in psychological research because of their technical difficulty, computational intensity, or large number of required data samples. These requirements impede the study of complexity in many areas of psychological science. A method is presented, tangle, which overcomes these difficulties and allows for complexity quantification in relatively short time series, such as those typically obtained from psychological studies. Tangle is a measure of how dissimilar a given process is from simple periodic motion. Tangle relies on the use of a three-dimensional time delay embedding of a one-dimensional time series. This embedding is then iteratively scaled and premultiplied by a modified upshift matrix until a convergence criterion is reached. The efficacy of tangle is shown on five mathematical time series and using emotional stability, anxiety time series data obtained from 65 socially anxious participants over a 5-week period, and positive affect time series derived from a single participant who experienced a major depression episode during measurement. Simulation results show tangle is able to distinguish between different complex temporal systems in time series with as few as 50 samples. Tangle shows promise as a reliable quantification of irregular behavior of a time series. Unlike many other complexity quantification metrics, tangle is technically simple to implement and is able to uncover meaningful information about time series derived from psychological research studies. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Emotion Malleability Beliefs and Emotion Experience and Regulation in the Daily Lives of People with High Trait Social Anxiety.

BACKGROUND: The extent to which a person believes they can change or control their own emotions is associated with trait-level symptoms of mood and anxiety-related psychopathology. Method: The present study examined how this belief relates to momentary and daily self-reports of affect, emotion regulation tendencies, and perceived effectiveness of emotion regulation attempts throughout a five-week experience sampling study conducted in N = 113 high socially anxious people (https://osf.io/eprwt/). RESULTS: Results suggest that people with relatively stronger beliefs that their emotions are malleable experienced more momentary and daily positive affect (relative to negative affect), even after controlling for social anxiety symptom severity (although only daily positive affect, and not momentary positive affect, remained significant after correcting for false discovery rate). However, emotion malleability beliefs were not uniquely associated with other emotion regulation-related outcomes in daily life, despite theory suggesting malleability beliefs influence motivation to engage in emotion regulation. CONCLUSION: The paucity of significant associations observed between trait malleability beliefs and momentary and daily self-reports of emotion regulation (despite consistent findings of such relationships at trait levels) calls for additional research to better understand the complex dynamics of emotion beliefs in daily life.