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Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.
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Desastres , Resiliencia Psicológica , Humanos , Pandemias , Salud Mental , MotivaciónRESUMEN
This editorial summary provides an overview of the 39th annual meeting of the International Society for Traumatic Stress Studies (ISTSS) held from November 1-4, 2023. The meeting, themed "Scalable Strategies to Address the Impact of Trauma Worldwide: Innovations and Implementation," encouraged presenters to focus on the scalability of traumatic stress research and practice. The articles presented at the meeting, which form this issue, cover a broad spectrum of topics, from basic biological mechanisms to innovative digital engagement to population-wide treatments. These articles emphasize eliminating biases in research design and psychiatric practice, promoting health equity, and addressing the unique challenges of traumatic stress. This summary underscores the importance of scalability in developing flexible, dynamic, and inclusive mental health care interventions.
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Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.
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Aprendizaje Automático , Salud Mental , HumanosRESUMEN
BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). METHODS: N = 81 patients admitted to an emergency department (ED) of a Level-1 Trauma Unit following a life-threatening traumatic event participated in an open-ended qualitative interview with a para-professional about their experience 1 month following admission. A deep neural network was utilized to extract facial features of emotion and their intensity, movement parameters, speech prosody, and natural language content. These features were utilized as inputs to classify PTSD and MDD cross-sectionally. RESULTS: Both video- and audio-based markers contributed to good discriminatory classification accuracy. The algorithm discriminates PTSD status at 1 month after ED admission with an AUC of 0.90 (weighted average precision = 0.83, recall = 0.84, and f1-score = 0.83) as well as depression status at 1 month after ED admission with an AUC of 0.86 (weighted average precision = 0.83, recall = 0.82, and f1-score = 0.82). CONCLUSIONS: Direct clinical observation during post-trauma free speech using deep learning identifies digital markers that can be utilized to classify MDD and PTSD status.
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Aprendizaje Profundo , Trastorno Depresivo Mayor , Trastornos por Estrés Postraumático , Nivel de Alerta , Depresión , Trastorno Depresivo Mayor/diagnóstico , Trastorno Depresivo Mayor/psicología , Humanos , Trastornos por Estrés Postraumático/diagnóstico , Trastornos por Estrés Postraumático/psicologíaRESUMEN
Posttraumatic stress disorder (PTSD) is prevalent and associated with significant morbidity. Mild traumatic brain injury (mTBI) concurrent with psychiatric trauma may be associated with PTSD. Prior studies of PTSD-related structural brain alterations have focused on military populations. The current study examined correlations between PTSD, acute mTBI, and structural brain alterations longitudinally in civilian patients (N = 504) who experienced a recent Criterion A traumatic event. Participants who reported loss of consciousness (LOC) were characterized as having mTBI; all others were included in the control group. PTSD symptoms were assessed at enrollment and over the following year; a subset of participants (n = 89) underwent volumetric brain MRI (M = 53 days posttrauma). Classes of PTSD symptom trajectories were modeled using latent growth mixture modeling. Associations between PTSD symptom trajectories and cortical thicknesses or subcortical volumes were assessed using a moderator-based regression. mTBI with LOC during trauma was positively correlated with the likelihood of developing a chronic PTSD symptom trajectory. mTBI showed significant interactions with cortical thickness in the rostral anterior cingulate cortex (rACC) in predicting PTSD symptoms, r = .461-.463. Bilateral rACC thickness positively predicted PTSD symptoms but only among participants who endorsed LOC, p < .001. The results demonstrate positive correlations between mTBI with LOC and PTSD symptom trajectories, and findings related to mTBI with LOC and rACC thickness interactions in predicting subsequent chronic PTSD symptoms suggest the importance of further understanding the role of mTBI in the context of PTSD to inform intervention and risk stratification.
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Conmoción Encefálica , Personal Militar , Trastornos por Estrés Postraumático , Encéfalo/diagnóstico por imagen , Conmoción Encefálica/complicaciones , Conmoción Encefálica/diagnóstico por imagen , Conmoción Encefálica/psicología , Humanos , Personal Militar/psicología , Trastornos por Estrés Postraumático/complicaciones , Trastornos por Estrés Postraumático/diagnóstico por imagen , Trastornos por Estrés Postraumático/psicología , Inconsciencia/diagnóstico por imagen , Inconsciencia/etiología , Inconsciencia/psicologíaRESUMEN
Research on posttraumatic psychopathology has focused primarily on posttraumatic stress disorder (PTSD); other posttraumatic psychiatric diagnoses are less well documented. The present study aimed to (a) develop a methodology to derive a cohort of individuals who experienced potentially traumatic events (PTEs) from registry-based data and (b) examine the risk of psychopathology within 5 years of experiencing a PTE. Using data from Danish national registries, we created a cohort of individuals with no age restrictions (range: 0-108 years) who experienced at least one of eight possible PTEs between 1994 and 2016 (N = 1,406,637). We calculated the 5-year incidence of nine categories of ICD-10 psychiatric disorders among this cohort and examined standardized morbidity ratios (SMRs) comparing the incidence of psychopathology in this group to the incidence in a nontraumatic stressor cohort (i.e., nonsuicide death of a relative; n = 423,270). Stress disorders (2.5%), substance use disorders (4.1%), and depressive disorders (3.0%) were the most common diagnoses following PTEs. Overall, the SMRs for the associations between any PTE and psychopathology varied from 1.9, 95% CI [1.9, 2.0], for stress disorders to 5.2, 95% CI [5.1. 5.3], for personality disorders. All PTEs except pregnancy-related trauma were associated with all forms of psychopathology. Associations were consistent regardless of whether a stress disorder was present. Traumatic experiences have a broad impact on psychiatric health. The present findings demonstrate one approach to capturing trauma exposure in medical record registry data. Increased traumatic experience characterization across studies will help improve the field's understanding of posttraumatic psychopathology.
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Trastornos por Estrés Postraumático , Trastornos de Estrés Traumático , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Preescolar , Dinamarca/epidemiología , Humanos , Lactante , Recién Nacido , Persona de Mediana Edad , Psicopatología , Sistema de Registros , Trastornos por Estrés Postraumático/epidemiología , Trastornos por Estrés Postraumático/psicología , Adulto JovenRESUMEN
Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts might offer opportunities for prevention. This case-cohort study used machine learning to examine sex-specific risk profiles for suicide attempts in Danish nationwide registry data. Cases were all persons who made a nonfatal suicide attempt between 1995 and 2015 (n = 22,974); the subcohort was a 5% random sample of the population at risk on January 1, 1995 (n = 265,183). We developed sex-stratified classification trees and random forests using 1,458 predictors, including demographic factors, family histories, psychiatric and physical health diagnoses, surgery, and prescribed medications. We found that substance use disorders/treatment, prescribed psychiatric medications, previous poisoning diagnoses, and stress disorders were important factors for predicting suicide attempts among men and women. Individuals in the top 5% of predicted risk accounted for 44.7% of all suicide attempts among men and 43.2% of all attempts among women. Our findings illuminate novel risk factors and interactions that are most predictive of nonfatal suicide attempts, while consistency between our findings and previous work in this area adds to the call to move machine learning suicide research toward the examination of high-risk subpopulations.
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Aprendizaje Automático , Intento de Suicidio/estadística & datos numéricos , Adolescente , Adulto , Dinamarca/epidemiología , Emigrantes e Inmigrantes/estadística & datos numéricos , Femenino , Estado de Salud , Humanos , Masculino , Trastornos Mentales/epidemiología , Salud Mental/estadística & datos numéricos , Persona de Mediana Edad , Sistema de Registros , Factores de Riesgo , Factores Sociodemográficos , Adulto JovenRESUMEN
BACKGROUND: Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. OBJECTIVE: We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. METHODS: We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. RESULTS: Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (ß=-0.68, P=.02, r2=0.40), overall expressivity (ß=-0.46, P=.10, r2=0.27), and head movement measured as head pitch variability (ß=-1.24, P=.006, r2=0.48) and head yaw variability (ß=-0.54, P=.06, r2=0.32). CONCLUSIONS: Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation.
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Ideación Suicida , Suicidio , Emociones , Humanos , Pacientes Internos , Factores de Riesgo , Intento de SuicidioRESUMEN
BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction. METHODS: N = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based. RESULTS: Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors. CONCLUSIONS: Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively.
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Cognición , Emociones , Función Ejecutiva , Aprendizaje Automático , Trastornos por Estrés Postraumático/fisiopatología , Trastornos por Estrés Postraumático/psicología , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Trastornos por Estrés Postraumático/diagnóstico , Adulto JovenRESUMEN
Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.
Spanish Abstracts by Asociación Chilena de Estrés Traumático (ACET) Aprendizaje de Máquinas para la Predicción del Estrés Postraumático y Resiliencia después del Trauma: Una Visión General de los Conceptos Básicos y Avances Recientes APRENDIZAJE DE MAQUINAS Y ESTRÉS POSTRAUMÁTICO Las respuestas al estrés postraumático se caracterizan por una heterogeneidad en el aspecto clínico y etiología. Esta heterogeneidad afecta la capacidad del campo para caracterizar, predecir y remediar respuestas desadaptativas al trauma. Los enfoques de aprendizaje maquinas (AM) son cada vez más utilizados para superar este problema fundamental en la caracterización, predicción y selección de tratamiento a través de las ramas de la medicina que han luchado con realidades clínicas similares de heterogeneidad en la etiología y resultados, como la oncología. En este artículo, revisamos y evaluamos los enfoques y las aplicaciones de AM utilizados en las áreas de estrés postraumático, patología del estrés, e investigación en resiliencia y presenta información didáctica y ejemplos para ayudar a investigadores interesados ââen la relevancia del AM para su propia investigación. Los estudios examinados ejemplifican el alto potencial de los enfoques de AM para construir modelos predictivos y de diagnóstico precisos de estrés postraumático y riesgo de estrés patológico basados ââen diversas fuentes de Información disponible. El uso de enfoques de AM para integrar datos multidimensionales demuestran ganancias sustanciales en la predicción del riesgo, incluso cuando las fuentes de datos son las mismas que las utilizadas en los modelos predictivos tradicionales. Esta área de investigación se beneficiará enormemente de la colaboración y el intercambio de datos entre los investigadores del trastorno de estrés postraumático, la patología del estrés y resiliencia.
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Aprendizaje Automático , Trastornos por Estrés Postraumático/diagnóstico , Humanos , Medición de Riesgo , Factores de RiesgoRESUMEN
Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. However, individual differences models (e.g., latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy, even when a single animal is included in the data set (i.e., n = 1 level). We further show an online software that enables the easy application of our method to any SigAA data set.
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Reacción de Prevención , Individualidad , Modelos Estadísticos , Pruebas Psicológicas , Programas Informáticos , Animales , Condicionamiento Psicológico , Interpretación Estadística de Datos , Masculino , Ratas Sprague-Dawley , Tiempo de Reacción , Reproducibilidad de los ResultadosRESUMEN
Posttraumatic stress disorder (PTSD) is common in the general population, yet there are limitations to the effectiveness, tolerability, and acceptability of available first-line interventions. We review the extant knowledge on the effects of marijuana and other cannabinoids on PTSD. Potential therapeutic effects of these agents may largely derive from actions on the endocannabinoid system and we review major animal and human findings in this area. Preclinical and clinical studies generally support the biological plausibility for cannabinoids' potential therapeutic effects, but underscore heterogeneity in outcomes depending on dose, chemotype, and individual variation. Treatment outcome studies of whole plant marijuana and related cannabinoids on PTSD are limited and not methodologically rigorous, precluding conclusions about their potential therapeutic effects. Reported benefits for nightmares and sleep (particularly with synthetic cannabinoid nabilone) substantiate larger controlled trials to determine effectiveness and tolerability. Of concern, marijuana use has been linked to adverse psychiatric outcomes, including conditions commonly comorbid with PTSD such as depression, anxiety, psychosis, and substance misuse. Available evidence is stronger for marijuana's harmful effects on the development of psychosis and substance misuse than for the development of depression and anxiety. Marijuana use is also associated with worse treatment outcomes in naturalistic studies, and with maladaptive coping styles that may maintain PTSD symptoms. Known risks of marijuana thus currently outweigh unknown benefits for PTSD. Although controlled research on marijuana and other cannabinoids' effects on PTSD remains limited, rapid shifts in the legal landscape may now enable such studies, potentially opening new avenues in PTSD treatment research.
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Cannabinoides/uso terapéutico , Marihuana Medicinal/uso terapéutico , Evaluación de Resultado en la Atención de Salud , Trastornos por Estrés Postraumático/tratamiento farmacológico , Animales , HumanosRESUMEN
Suicide rates among recent veterans have led to interest in risk identification. Evidence of gender-and trauma-specific predictors of suicidal ideation necessitates the use of advanced computational methods capable of elucidating these important and complex associations. In this study, we used machine learning to examine gender-specific associations between predeployment and military factors, traumatic deployment experiences, and psychopathology and suicidal ideation (SI) in a national sample of veterans deployed during the Iraq and Afghanistan conflicts (n = 2,244). Classification, regression tree analyses, and random forests were used to identify associations with SI and determine their classification accuracy. Findings converged on several associations for men that included depression, posttraumatic stress disorder (PTSD), and somatic complaints. Sexual harassment during deployment emerged as a key factor that interacted with PTSD and depression and demonstrated a stronger association with SI among women. Classification accuracy for SI presence or absence was good based on the receiver operating characteristic area under the curve, men = .91, women = .92. The risk for SI was classifiable with good accuracy, with associations that varied by gender. The use of machine learning analyses allowed for the discovery of rich, nuanced results that should be replicated in other samples and may eventually be a basis for the development of gender-specific actuarial tools to assess SI risk among veterans.
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Modelos Psicológicos , Ideación Suicida , Veteranos/psicología , Adulto , Campaña Afgana 2001- , Área Bajo la Curva , Depresión/psicología , Femenino , Humanos , Guerra de Irak 2003-2011 , Aprendizaje Automático , Masculino , Persona de Mediana Edad , Curva ROC , Medición de Riesgo/métodos , Factores de Riesgo , Factores Sexuales , Acoso Sexual/psicología , Trastornos por Estrés Postraumático/psicología , Adulto JovenRESUMEN
BACKGROUND: Traumatic injuries affect millions of patients each year, and resulting post-traumatic stress disorder (PTSD) significantly contributes to subsequent impairment. AIMS: To map the distinctive long-term trajectories of PTSD responses over 6 years by using latent growth mixture modelling. METHOD: Randomly selected injury patients (n = 1084) admitted to four hospitals around Australia were assessed in hospital, and at 3, 12, 24 and 72 months. Lifetime psychiatric history and current PTSD severity and funxctioning were assessed. RESULTS: Five trajectories of PTSD response were noted across the 6 years: (a) chronic (4%), (b) recovery (6%), (c) worsening/recovery (8%), (d) worsening (10%) and (e) resilient (73%). A poorer trajectory was predicted by female gender, recent life stressors, presence of mild traumatic brain injury and admission to intensive care unit. CONCLUSIONS: These findings demonstrate the long-term PTSD effects that can occur following traumatic injury. The different trajectories highlight that monitoring a subset of patients over time is probably a more accurate means of identifying PTSD rather than relying on factors that can be assessed during hospital admission.
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Lesiones Encefálicas/epidemiología , Lesiones Encefálicas/psicología , Índice de Severidad de la Enfermedad , Trastornos por Estrés Postraumático/diagnóstico , Adolescente , Adulto , Anciano , Australia , Femenino , Estudios de Seguimiento , Humanos , Acontecimientos que Cambian la Vida , Masculino , Persona de Mediana Edad , Factores de Riesgo , Adulto JovenRESUMEN
BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators. METHODS: Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. RESULTS: The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. CONCLUSIONS: Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.
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Adaptación Psicológica/fisiología , Inteligencia Artificial , Trastornos por Estrés Postraumático , Heridas y Lesiones , Adulto , Algoritmos , Diagnóstico Precoz , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pronóstico , Curva ROC , Medición de Riesgo , Factores de Riesgo , Trastornos por Estrés Postraumático/diagnóstico , Trastornos por Estrés Postraumático/etiología , Trastornos por Estrés Postraumático/fisiopatología , Trastornos por Estrés Postraumático/prevención & control , Investigación Biomédica Traslacional , Heridas y Lesiones/complicaciones , Heridas y Lesiones/psicologíaRESUMEN
Kalisch and colleagues identify several routes to a better understanding of mechanisms underlying resilience and highlight the need to integrate findings from neuroscience and animal learning. We argue that appreciating methodological complexity and integrating neurobiological perspectives will advance the science of resilience and ultimately help improve the lives of those exposed to stress and adversity.
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Aprendizaje , Neurobiología , Animales , Humanos , NeurocienciasRESUMEN
The course of depression in relation to myocardial infarction (MI), commonly known as heart attack, and the consequences for mortality are not well characterized. Further, optimism may predict both the effects of MI on depression as well as mortality secondary to MI. In the current study, we utilized a large population-based prospective sample of older adults (N=2,147) to identify heterogeneous trajectories of depression from 6 years prior to their first-reported MI to 4 years after. Findings indicated that individuals were at significantly increased risk for mortality when depression emerged after their first-reported MI, compared with resilient individuals who had no significant post-MI elevation in depression symptomatology. Individuals with chronic depression and those demonstrating pre-event depression followed by recovery after MI were not at increased risk. Further, optimism, measured before MI, prospectively differentiated all depressed individuals from participants who were resilient.
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Afecto , Trastorno Depresivo/mortalidad , Trastorno Depresivo/psicología , Infarto del Miocardio/mortalidad , Infarto del Miocardio/psicología , Anciano , Comorbilidad , Femenino , Estudios de Seguimiento , Humanos , Masculino , Estudios Prospectivos , Resiliencia Psicológica , Riesgo , Estados Unidos/epidemiologíaRESUMEN
BACKGROUND: Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. OBJECTIVE: This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. METHODS: This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. RESULTS: The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. CONCLUSIONS: The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42547.
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Trastornos de Ansiedad , Terapia Cognitivo-Conductual , Teléfono Inteligente , Adulto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Trastornos de Ansiedad/terapia , Trastornos de Ansiedad/diagnóstico , Terapia Cognitivo-Conductual/métodos , Psicoterapia/métodos , Resultado del Tratamiento , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.