Individualized Machine-learning-based Clinical Assessment Recommendation System.

Background: Traditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments. Methods: Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features. Findings: The iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1-3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0·999 and an AUC of 1·000, outperforming the Global approach with an accuracy of 0·689 and an AUC of 0·639. In the early diabetes dataset, iCARE shows improvements of 1·5-3·5% in accuracy and AUC across different numbers of initial features. Conversely, in synthetic datasets 4-5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics. Interpretation: iCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses. Funding: This work was supported by startup funding from the Department of Psychology at the University of Kansas provided to A.A., and the R01MH125740 award from NIH partially supported J.M.G.'s work.

Are within- and between-session changes in distress associated with treatment outcomes? Findings from two clinical trials of exposure for eating disorders.

OBJECTIVE: Imaginal exposure is a novel intervention for eating disorders (EDs) that has been investigated as a method for targeting ED symptoms and fears. Research is needed to understand mechanisms of change during imaginal exposure for EDs, including whether within- and between-session distress reduction is related to treatment outcomes. METHOD: Study 1 tested four sessions of online imaginal exposure (N = 143). Study 2 examined combined imaginal and in vivo exposure, comprising six imaginal exposure sessions (N = 26). ED symptoms and fears were assessed pre- and posttreatment, and subjective distress and state anxiety were collected during sessions. RESULTS: Subjective distress tended to increase within-session in both studies, and within-session reduction was not associated with change in ED symptoms or fears. In Study 1, between-session reduction of distress and state anxiety was associated with greater decreases in ED symptoms and fears pre-to posttreatment. In Study 2, between-session distress reduction occurred but was not related to outcomes. CONCLUSIONS: Within-session distress reduction may not promote change during exposure for EDs, whereas between-session distress reduction may be associated with better treatment outcomes. These findings corroborate research on distress reduction during exposure for anxiety disorders. Clinicians might consider approaches to exposure-based treatment that focus on distress tolerance and promote between-session distress reduction.

Associations between transdiagnostic traits of psychopathology and hybrid posttraumatic stress disorder factors in a trauma-exposed community sample.

Dimensional conceptualizations of psychopathology hold promise for understanding the high rates of comorbidity with posttraumatic stress disorder (PTSD). Linking PTSD symptoms to transdiagnostic dimensions of psychopathology may enable researchers and clinicians to understand the patterns and breadth of behavioral sequelae following traumatic experiences that may be shared with other psychiatric disorders. To explore this premise, we recruited a trauma-exposed online community sample (N = 462) and measured dimensional transdiagnostic traits of psychopathology using parceled facets derived from the Personality Inventory for DSM-5 Faceted-Short Form. PTSD symptom factors were measured using the PTSD Checklist for DSM-5 and derived using confirmatory factor analysis according to the seven-factor hybrid model (i.e., Intrusions, Avoidance, Negative Affect, Anhedonia, Externalizing Behaviors, Anxious Arousal, And Dysphoric Arousal). We observed hypothesized associations between PTSD factors and transdiagnostic traits indicating that some transdiagnostic dimensions were associated with nearly all PTSD symptom factors (e.g., emotional lability: rmean = .35), whereas others showed more unique relationships (e.g., hostility-Externalizing Behavior: r = .60; hostility with other PTSD factors: rs = .12-.31). All PTSD factors were correlated with traits beyond those that would appear to be construct-relevant, suggesting the possibility of indirect associations that should be explicated in future research. The results indicate the breadth of trait-like consequences associated with PTSD symptom exacerbation, with implications for case conceptualization and treatment planning. Although PTSD is not a personality disorder, the findings indicate that increased PTSD factor severity is moderately associated with different patterns of trait-like disruptions in many areas of functioning.

Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity.

A growing body of research suggests that movement aids facial expression recognition. However, less is known about the conditions under which the dynamic advantage occurs. The aim of this research was to test emotion recognition in static and dynamic facial expressions, thereby exploring the role of three featural parameters (prototypicality, ambiguity, and complexity) in human and machine analysis. In two studies, facial expression videos and corresponding images depicting the peak of the target and non-target emotion were presented to human observers and the machine classifier (FACET). Results revealed higher recognition rates for dynamic stimuli compared to non-target images. Such benefit disappeared in the context of target-emotion images which were similarly well (or even better) recognised than videos, and more prototypical, less ambiguous, and more complex in appearance than non-target images. While prototypicality and ambiguity exerted more predictive power in machine performance, complexity was more indicative of human emotion recognition. Interestingly, recognition performance by the machine was found to be superior to humans for both target and non-target images. Together, the findings point towards a compensatory role of dynamic information, particularly when static-based stimuli lack relevant features of the target emotion. Implications for research using automatic facial expression analysis (AFEA) are discussed.

Randomized trial of brief interpersonal psychotherapy and cognitive behavioral therapy for depression delivered both in-person and by telehealth.

BACKGROUND: Expert consensus guidelines recommend Cognitive Behavioral Therapy (CBT) and Interpersonal Psychotherapy (IPT), interventions that were historically delivered face-to-face, as first-line treatments for Major Depressive Disorder (MDD). Despite the ubiquity of telehealth following the COVID-19 pandemic, little is known about differential outcomes with CBT versus IPT delivered in-person (IP) or via telehealth (TH) or whether working alliance is affected. METHODS: Adults meeting DSM-5 criteria for MDD were randomly assigned to either 8 sessions of IPT or CBT (group). Mid-trial, COVID-19 forced a change of therapy delivery from IP to TH (study phase). We compared changes in Hamilton Rating Scale for Depression (HRSD-17) and Working Alliance Inventory (WAI) scores for individuals by group and phase: CBT-IP (n = 24), CBT-TH (n = 11), IPT-IP (n = 25) and IPT-TH (n = 17). RESULTS: HRSD-17 scores declined significantly from pre to post treatment (pre: M = 17.7, SD = 4.4 vs. post: M = 11.7, SD = 5.9; p < .001; d = 1.45) without significant group or phase effects. WAI scores did not differ by group or phase. Number of completed therapy sessions was greater for TH (M = 7.8, SD = 1.2) relative to IP (M = 7.2, SD = 1.6) (Mann-Whitney U = 387.50, z = -2.24, p = .025). LIMITATIONS: Participants were not randomly assigned to IP versus TH. Sample size is small. CONCLUSIONS: This study provides preliminary evidence supporting the efficacy of both brief IPT and CBT, delivered by either TH or IP, for depression. It showed that working alliance is preserved in TH, and delivery via TH may improve therapy adherence. Prospective, randomized controlled trials are needed to definitively test efficacy of brief IPT and CBT delivered via TH versus IP.

DynAMoS: The Dynamic Affective Movie Clip Database for Subjectivity Analysis.

In this paper, we describe the design, collection, and validation of a new video database that includes holistic and dynamic emotion ratings from 83 participants watching 22 affective movie clips. In contrast to previous work in Affective Computing, which pursued a single "ground truth" label for the affective content of each moment of each video (e.g., by averaging the ratings of 2 to 7 trained participants), we embrace the subjectivity inherent to emotional experiences and provide the full distribution of all participants' ratings (with an average of 76.7 raters per video). We argue that this choice represents a paradigm shift with the potential to unlock new research directions, generate new hypotheses, and inspire novel methods in the Affective Computing community. We also describe several interdisciplinary use cases for the database: to provide dynamic norms for emotion elicitation studies (e.g., in psychology, medicine, and neuroscience), to train and test affective content analysis algorithms (e.g., for dynamic emotion recognition, video summarization, and movie recommendation), and to study subjectivity in emotional reactions (e.g., to identify moments of emotional ambiguity or ambivalence within movies, identify predictors of subjectivity, and develop personalized affective content analysis algorithms). The database is made freely available to researchers for noncommercial use at https://dynamos.mgb.org.

Weighting schemes and incomplete data: A generalized Bayesian framework for chance-corrected interrater agreement.

Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Toward Causal Understanding of Therapist-Client Relationships: A Study of Language Modality and Social Entrainment.

The relationship between a therapist and their client is one of the most critical determinants of successful therapy. The working alliance is a multifaceted concept capturing the collaborative aspect of the therapist-client relationship; a strong working alliance has been extensively linked to many positive therapeutic outcomes. Although therapy sessions are decidedly multimodal interactions, the language modality is of particular interest given its recognized relationship to similar dyadic concepts such as rapport, cooperation, and affiliation. Specifically, in this work we study language entrainment, which measures how much the therapist and client adapt toward each other's use of language over time. Despite the growing body of work in this area, however, relatively few studies examine causal relationships between human behavior and these relationship metrics: does an individual's perception of their partner affect how they speak, or does how they speak affect their perception? We explore these questions in this work through the use of structural equation modeling (SEM) techniques, which allow for both multilevel and temporal modeling of the relationship between the quality of the therapist-client working alliance and the participants' language entrainment. In our first experiment, we demonstrate that these techniques perform well in comparison to other common machine learning models, with the added benefits of interpretability and causal analysis. In our second analysis, we interpret the learned models to examine the relationship between working alliance and language entrainment and address our exploratory research questions. The results reveal that a therapist's language entrainment can have a significant impact on the client's perception of the working alliance, and that the client's language entrainment is a strong indicator of their perception of the working alliance. We discuss the implications of these results and consider several directions for future work in multimodality.

Computational analysis of spoken language in acute psychosis and mania.

OBJECTIVES: This study aimed to (1) determine the feasibility of collecting behavioral data from participants hospitalized with acute psychosis and (2) begin to evaluate the clinical information that can be computationally derived from such data. METHODS: Behavioral data was collected across 99 sessions from 38 participants recruited from an inpatient psychiatric unit. Each session started with a semi-structured interview modeled on a typical "clinical rounds" encounter and included administration of the Positive and Negative Syndrome Scale (PANSS). ANALYSIS: We quantified aspects of participants' verbal behavior during the interview using lexical, coherence, and disfluency features. We then used two complementary approaches to explore our second objective. The first approach used predictive models to estimate participants' PANSS scores from their language features. Our second approach used inferential models to quantify the relationships between individual language features and symptom measures. RESULTS: Our predictive models showed promise but lacked sufficient data to achieve clinically useful accuracy. Our inferential models identified statistically significant relationships between numerous language features and symptom domains. CONCLUSION: Our interview recording procedures were well-tolerated and produced adequate data for transcription and analysis. The results of our inferential modeling suggest that automatic measurements of expressive language contain signals highly relevant to the assessment of psychosis. These findings establish the potential of measuring language during a clinical interview in a naturalistic setting and generate specific hypotheses that can be tested in future studies. This, in turn, will lead to more accurate modeling and better understanding of the relationships between expressive language and psychosis.

Reconsidering the Duchenne Smile: Formalizing and Testing Hypotheses about Eye Constriction and Positive Emotion.

The common view of emotional expressions is that certain configurations of facial-muscle movements reliably reveal certain categories of emotion. The principal exemplar of this view is the Duchenne smile, a configuration of facial-muscle movements (i.e., smiling with eye constriction) that has been argued to reliably reveal genuine positive emotion. In this paper, we formalized a list of hypotheses that have been proposed regarding the Duchenne smile, briefly reviewed the literature weighing on these hypotheses, identified limitations and unanswered questions, and conducted two empirical studies to begin addressing these limitations and answering these questions. Both studies analyzed a database of 751 smiles observed while 136 participants completed experimental tasks designed to elicit amusement, embarrassment, fear, and physical pain. Study 1 focused on participants' self-reported positive emotion and Study 2 focused on how third-party observers would perceive videos of these smiles. Most of the hypotheses that have been proposed about the Duchenne smile were either contradicted by or only weakly supported by our data. Eye constriction did provide some information about experienced positive emotion, but this information was lacking in specificity, already provided by other smile characteristics, and highly dependent on context. Eye constriction provided more information about perceived positive emotion, including some unique information over other smile characteristics, but context was also important here as well. Overall, our results suggest that accurately inferring positive emotion from a smile requires more sophisticated methods than simply looking for the presence/absence (or even the intensity) of eye constriction.

Goals, Tasks, and Bonds: Toward the Computational Assessment of Therapist Versus Client Perception of Working Alliance.

Early client dropout is one of the most significant challenges facing psychotherapy: recent studies suggest that at least one in five clients will leave treatment prematurely. Clients may terminate therapy for various reasons, but one of the most common causes is the lack of a strong working alliance. The concept of working alliance captures the collaborative relationship between a client and their therapist when working toward the progress and recovery of the client seeking treatment. Unfortunately, clients are often unwilling to directly express dissatisfaction in care until they have already decided to terminate therapy. On the other side, therapists may miss subtle signs of client discontent during treatment before it is too late. In this work, we demonstrate that nonverbal behavior analysis may aid in bridging this gap. The present study focuses primarily on the head gestures of both the client and therapist, contextualized within conversational turn-taking actions between the pair during psychotherapy sessions. We identify multiple behavior patterns suggestive of an individual's perspective on the working alliance; interestingly, these patterns also differ between the client and the therapist. These patterns inform the development of predictive models for self-reported ratings of working alliance, which demonstrate significant predictive power for both client and therapist ratings. Future applications of such models may stimulate preemptive intervention to strengthen a weak working alliance, whether explicitly attempting to repair the existing alliance or establishing a more suitable client-therapist pairing, to ensure that clients encounter fewer barriers to receiving the treatment they need.

A Bayesian multilevel analysis of the longitudinal associations between relationship quality and suicidal ideation and attempts among youth with bipolar disorder.

BACKGROUND: Youth with bipolar disorder (BD) are at high risk for suicidal thoughts and behaviors and frequently experience interpersonal impairment, which is a risk factor for suicide. Yet, no study to date has examined the longitudinal associations between relationship quality in family/peer domains and suicidal thoughts and behaviors among youth with BD. Thus, we investigated how between-person differences - reflecting the average relationship quality across time - and within-person changes, reflecting recent fluctuations in relationship quality, act as distal and/or proximal risk factors for suicidal ideation (SI) and suicide attempts. METHODS: We used longitudinal data from the Course and Outcome of Bipolar Youth Study (N = 413). Relationship quality variables were decomposed into stable (i.e., average) and varying (i.e., recent) components and entered, along with major clinical covariates, into separate Bayesian multilevel models predicting SI and suicide attempt. We also examined how the relationship quality effects interacted with age and sex. RESULTS: Poorer average relationship quality with parents (ß = -.33, 95% Bayesian highest density interval (HDI) [-0.54, -0.11]) or friends (ß = -.33, 95% HDI [-0.55, -0.11]) was longitudinally associated with increased risk of SI but not suicide attempt. Worsening recent relationship quality with parents (ß = -.10, 95% HDI [-0.19, -0.03]) and, to a lesser extent, friends (ß = -.06, 95% HDI [-0.15, 0.03]) was longitudinally associated with increased risk of SI, but only worsening recent relationship quality with parents was also associated with increased risk of suicide attempt (ß = -.15, 95% HDI [-0.31, 0.01]). The effects of certain relationship quality variables were moderated by gender but not age. CONCLUSIONS: Among youth with BD, having poorer average relationship quality with peers and/or parents represents a distal risk factor for SI but not suicide attempts. Additionally, worsening recent relationship quality with parents may be a time-sensitive indicator of increased risk for SI or suicide attempt.

Depression Severity Assessment for Adolescents at High Risk of Mental Disorders.

Recent progress in artificial intelligence has led to the development of automatic behavioral marker recognition, such as facial and vocal expressions. Those automatic tools have enormous potential to support mental health assessment, clinical decision making, and treatment planning. In this paper, we investigate nonverbal behavioral markers of depression severity assessed during semi-structured medical interviews of adolescent patients. The main goal of our research is two-fold: studying a unique population of adolescents at high risk of mental disorders and differentiating mild depression from moderate or severe depression. We aim to explore computationally inferred facial and vocal behavioral responses elicited by three segments of the semi-structured medical interviews: Distress Assessment Questions, Ubiquitous Questions, and Concept Questions. Our experimental methodology reflects best practise used for analyzing small sample size and unbalanced datasets of unique patients. Our results show a very interesting trend with strongly discriminative behavioral markers from both acoustic and visual modalities. These promising results are likely due to the unique classification task (mild depression vs. moderate and severe depression) and three types of probing questions.

Toward Multimodal Modeling of Emotional Expressiveness.

Emotional expressiveness captures the extent to which a person tends to outwardly display their emotions through behavior. Due to the close relationship between emotional expressiveness and behavioral health, as well as the crucial role that it plays in social interaction, the ability to automatically predict emotional expressiveness stands to spur advances in science, medicine, and industry. In this paper, we explore three related research questions. First, how well can emotional expressiveness be predicted from visual, linguistic, and multimodal behavioral signals? Second, how important is each behavioral modality to the prediction of emotional expressiveness? Third, which behavioral signals are reliably related to emotional expressiveness? To answer these questions, we add highly reliable transcripts and human ratings of perceived emotional expressiveness to an existing video database and use this data to train, validate, and test predictive models. Our best model shows promising predictive performance on this dataset (RMSE = 0.65, R 2 = 0.45, r = 0.74). Multimodal models tend to perform best overall, and models trained on the linguistic modality tend to outperform models trained on the visual modality. Finally, examination of our interpretable models' coefficients reveals a number of visual and linguistic behavioral signals-such as facial action unit intensity, overall word count, and use of words related to social processes-that reliably predict emotional expressiveness.

Properties of the Continuous Assessment of Interpersonal Dynamics Across Sex, Level of Familiarity, and Interpersonal Conflict.

The Continuous Assessment of Interpersonal Dynamics (CAID) is a method in which trained observers continuously code the dominance and warmth of individuals who interact with one another in dyads. This method has significant promise for assessing dynamic interpersonal processes. The purpose of this study was to examine the impact of individual sex, dyadic familiarity, and situational conflict on patterns of interpersonal warmth, dominance, and complementarity as assessed via CAID. We used six samples with 603 dyads, including two samples of unacquainted mixed-sex undergraduates interacting in a collaborative task, two samples of couples interacting in both collaborative and conflict tasks, and two samples of mothers and children interacting in both collaborative and conflict tasks. Complementarity effects were robust across all samples, and individuals tended to be relatively warm and dominant. Results from multilevel models indicated that women were slightly warmer than men, whereas there were no sex differences in dominance. Unfamiliar dyads and dyads interacting in more collaborative tasks were relatively warmer, more submissive, and more complementary on warmth but less complementary on dominance. These findings speak to the utility of the CAID method for assessing interpersonal dynamics and provide norms for researchers who use the method for different types of samples and applications.

Alexithymia - Not autism - is associated with frequency of social interactions in adults.

OBJECTIVE: While much is known about the quality of social behavior among neurotypical individuals and those with autism spectrum disorder (ASD), little work has evaluated quantity of social interactions. This study used ecological momentary assessment (EMA) to quantify in vivo daily patterns of social interaction in adults as a function of demographic and clinical factors. METHOD: Adults with and without ASD (NASD = 23, NNeurotypical = 52) were trained in an EMA protocol to report their social interactions via smartphone over one week. Participants completed measures of IQ, ASD symptom severity and alexithymia symptom severity. RESULTS: Cyclical multilevel models were used to account for nesting of observations. Results suggest a daily cyclical pattern of social interaction that was robust to ASD and alexithymia symptoms. Adults with ASD did not have fewer social interactions than neurotypical peers; however, severity of alexithymia symptoms predicted fewer social interactions regardless of ASD status. CONCLUSIONS: These findings suggest that alexithymia, not ASD severity, may drive social isolation and highlight the need to reevaluate previously accepted notions regarding differences in social behavior utilizing modern methods.

Narcissistic Admiration and Rivalry: An Interpersonal Approach to Construct Validation.

The present study applied the interpersonal perspective in testing the narcissistic admiration and rivalry concept (NARC) and examining the construct validity of the corresponding Narcissistic Admiration and Rivalry Questionnaire (NARQ). Two undergraduate samples (Sample 1: N = 290; Sample 2: N = 188) completed self-report measures of interpersonal processes based in the interpersonal circumplex (IPC), as well as measures of related constructs. In examining IPC correlates, the authors used a novel bootstrapping approach to determine if admiration and rivalry related to differing interpersonal profiles. Consistent with the authors' hypotheses, admiration was distinctly related to generally agentic (i.e., dominant) interpersonal processes, whereas rivalry generally reflected (low) communal (i.e., hostile) interpersonal processes. Furthermore, NARQ-admiration and NARQ-rivalry related to generally adaptive and maladaptive aspects of status-related constructs, emotional, personality, and social adjustment, respectively. This research provides further support for the NARC, as well as construct validation for the NARQ.

Reconsidering the Duchenne Smile: Indicator of Positive Emotion or Artifact of Smile Intensity?

The Duchenne smile hypothesis is that smiles that include eye constriction (AU6) are the product of genuine positive emotion, whereas smiles that do not are either falsified or related to negative emotion. This hypothesis has become very influential and is often used in scientific and applied settings to justify the inference that a smile is either true or false. However, empirical support for this hypothesis has been equivocal and some researchers have proposed that, rather than being a reliable indicator of positive emotion, AU6 may just be an artifact produced by intense smiles. Initial support for this proposal has been found when comparing smiles related to genuine and feigned positive emotion; however, it has not yet been examined when comparing smiles related to genuine positive and negative emotion. The current study addressed this gap in the literature by examining spontaneous smiles from 136 participants during the elicitation of amusement, embarrassment, fear, and pain (from the BP4D+ dataset). Bayesian multilevel regression models were used to quantify the associations between AU6 and self-reported amusement while controlling for smile intensity. Models were estimated to infer amusement from AU6 and to explain the intensity of AU6 using amusement. In both cases, controlling for smile intensity substantially reduced the hypothesized association, whereas the effect of smile intensity itself was quite large and reliable. These results provide further evidence that the Duchenne smile is likely an artifact of smile intensity rather than a reliable and unique indicator of genuine positive emotion.

The Association Between Daily Posttraumatic Stress Symptoms and Pain Over the First 14 Days After Injury: An Experience Sampling Study.

OBJECTIVES: Psychosocial factors and responses to injury modify the transition from acute to chronic pain. Specifically, posttraumatic stress disorder (PTSD) symptoms (reexperiencing, avoidance, and hyperarousal symptoms) exacerbate and cooccur with chronic pain. Yet no study has prospectively considered the associations among these psychological processes and pain reports using experience sampling methods (ESMs) during the acute aftermath of injury. This study applied ESM via daily text messaging to monitor and detect relationships among psychosocial factors and postinjury pain across the first 14 days after emergency department (ED) discharge. METHODS: We recruited 75 adults (59% male; mean ± SD age = 34 ± 11.73 years) who experienced a potentially traumatic injury (i.e., involving life threat or serious injury) in the past 24 hours from the EDs of two Level I trauma centers. Participants received five questions per day via text messaging from Day 1 to Day 14 post-ED discharge; three questions measured PTSD symptoms, one question measured perceived social support, and one question measured physical pain. RESULTS: Sixty-seven participants provided sufficient data for inclusion in the final analyses, and the average response rate per subject was 86%. Pain severity score decreased from a mean ± SD of 7.2 ± 2.0 to 4.4 ± 2.69 over 14 days and 50% of the variance in daily pain scores was within person. In multilevel structural equation models, pain scores decreased over time, and daily fluctuations of hyperarousal (B = 0.22, 95% confidetnce interval = 0.08-0.36) were uniquely associated with daily fluctuations in reported pain level within each person. CONCLUSIONS: Daily hyperarousal symptoms predict same-day pain severity over the acute postinjury recovery period. We also demonstrated feasibility to screen and identify patients at risk for pain chronicity in the acute aftermath of injury. Early interventions aimed at addressing hyperarousal (e.g., anxiolytics) could potentially aid in reducing experience of pain.

DARMA: Software for dual axis rating and media annotation.

Continuous measurement systems provide a means of measuring dynamic behavioral and experiential processes as they play out over time. DARMA is a modernized continuous measurement system that synchronizes media playback and the continuous recording of two-dimensional measurements. These measurements can be observational or self-reported and are provided in real-time through the manipulation of a computer joystick. DARMA also provides tools for reviewing and comparing collected measurements and for customizing various settings. DARMA is a domain-independent software tool that was designed to aid researchers who are interested in gaining a deeper understanding of behavior and experience. It is especially well-suited to the study of affective and interpersonal processes, such as the perception and expression of emotional states and the communication of social signals. DARMA is open-source using the GNU General Public License (GPL) and is available for free download from http://darma.jmgirard.com .