Feasibility, Acceptability, and Preliminary Efficacy of a Smartphone App-Led Cognitive Behavioral Therapy for Depression Under Therapist Supervision: Open Trial.

BACKGROUND: Major depressive disorder affects approximately 1 in 5 adults during their lifetime and is the leading cause of disability worldwide. Yet, a minority receive adequate treatment due to person-level (eg, geographical distance to providers) and systems-level (eg, shortage of trained providers) barriers. Digital tools could improve this treatment gap by reducing the time and frequency of therapy sessions needed for effective treatment through the provision of flexible, automated support. OBJECTIVE: This study aimed to examine the feasibility, acceptability, and preliminary clinical effect of Mindset for Depression, a deployment-ready 8-week smartphone-based cognitive behavioral therapy (CBT) supported by brief teletherapy appointments with a therapist. METHODS: This 8-week, single-arm open trial tested the Mindset for Depression app when combined with 8 brief (16-25 minutes) video conferencing visits with a licensed doctoral-level CBT therapist (n=28 participants). The app offers flexible, accessible psychoeducation, CBT skills practice, and support to patients as well as clinician guidance to promote sustained engagement, monitor safety, and tailor treatment to individual patient needs. To increase accessibility and thus generalizability, all study procedures were conducted remotely. Feasibility and acceptability were assessed via attrition, patient expectations and feedback, and treatment utilization. The primary clinical outcome measure was the clinician-rated Hamilton Depression Rating Scale, administered at pretreatment, midpoint, and posttreatment. Secondary measures of functional impairment and quality of life as well as maintenance of gains (3-month follow-up) were also collected. RESULTS: Treatment credibility (week 4), expectancy (week 4), and satisfaction (week 8) were moderate to high, and attrition was low (n=2, 7%). Participants self-reported using the app or practicing (either on or off the app) the CBT skills taught in the app for a median of 50 (IQR 30-60; week 4) or 60 (IQR 30-90; week 8) minutes per week; participants accessed the app on an average 36.8 (SD 10.0) days and completed a median of 7 of 8 (IQR 6-8) steps by the week 8 assessment. The app was rated positively across domains of engagement, functionality, aesthetics, and information. Participants' depression severity scores decreased from an average Hamilton Depression Rating Scale score indicating moderate depression (mean 19.1, SD 5.0) at baseline to a week 8 mean score indicating mild depression (mean 10.8, SD 6.1; d=1.47; P<.001). Improvement was also observed for functional impairment and quality of life. Gains were maintained at 3-month follow-up. CONCLUSIONS: The results show that Mindset for Depression is a feasible and acceptable treatment option for individuals with major depressive disorder. This smartphone-led treatment holds promise to be an efficacious, scalable, and cost-effective treatment option. The next steps include testing Mindset for Depression in a fully powered randomized controlled trial and real-world clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05386329; https://clinicaltrials.gov/study/NCT05386329?term=NCT05386329.

Substance Use, Suicidal Thoughts, and Psychiatric Comorbidities Among High School Students.

This cross-sectional study evaluates the dose-dependent association between alcohol, cannabis, and nicotine use and psychiatric symptoms among participants in the Substance Use and Risk Factor Survey and the Youth Risk Behavior Survey.

Determining Distinct Suicide Attempts From Recurrent Electronic Health Record Codes: Classification Study.

BACKGROUND: Prior suicide attempts are a relatively strong risk factor for future suicide attempts. There is growing interest in using longitudinal electronic health record (EHR) data to derive statistical risk prediction models for future suicide attempts and other suicidal behavior outcomes. However, model performance may be inflated by a largely unrecognized form of "data leakage" during model training: diagnostic codes for suicide attempt outcomes may refer to prior attempts that are also included in the model as predictors. OBJECTIVE: We aimed to develop an automated rule for determining when documented suicide attempt diagnostic codes identify distinct suicide attempt events. METHODS: From a large health care system's EHR, we randomly sampled suicide attempt codes for 300 patients with at least one pair of suicide attempt codes documented at least one but no more than 90 days apart. Supervised chart reviewers assigned the clinical settings (ie, emergency department [ED] versus non-ED), methods of suicide attempt, and intercode interval (number of days). The probability (or positive predictive value) that the second suicide attempt code in a given pair of codes referred to a distinct suicide attempt event from its preceding suicide attempt code was calculated by clinical setting, method, and intercode interval. RESULTS: Of 1015 code pairs reviewed, 835 (82.3%) were nonindependent (ie, the 2 codes referred to the same suicide attempt event). When the second code in a pair was documented in a clinical setting other than the ED, it represented a distinct suicide attempt 3.3% of the time. The more time elapsed between codes, the more likely the second code in a pair referred to a distinct suicide attempt event from its preceding code. Code pairs in which the second suicide attempt code was assigned in an ED at least 5 days after its preceding suicide attempt code had a positive predictive value of 0.90. CONCLUSIONS: EHR-based suicide risk prediction models that include International Classification of Diseases codes for prior suicide attempts as a predictor may be highly susceptible to bias due to data leakage in model training. We derived a simple rule to distinguish codes that reflect new, independent suicide attempts: suicide attempt codes documented in an ED setting at least 5 days after a preceding suicide attempt code can be confidently treated as new events in EHR-based suicide risk prediction models. This rule has the potential to minimize upward bias in model performance when prior suicide attempts are included as predictors in EHR-based suicide risk prediction models.

A roadmap to address stress in graduate students: How to develop and disseminate a student-led single-session evidence based intervention.

Objective: This manuscript describes an evidence-based, student-led, single-session group intervention to support emotional wellbeing among graduate students. The present objective is to provide a roadmap for other universities. Participants: Key participants include clinical psychology graduate students (leader and workshop facilitators), faculty supervisor, representatives from receiving departments or schools, and institutional advocates. Methods: The two-hour workshop was based on four core transdiagnostic cognitive behavioral skills, including psychoeducation about emotions, mindful emotional awareness, cognitive flexibility, and behavior change. The workshop was designed and continues to be led by trained graduate students. Results: Key steps and lessons learned are presented for the exploration, preparation, implementation, and sustainment phases. Conclusions: This program has the potential to be flexibly replicated at other universities to assist with graduate student mental health. It provides unique supports for recipients and unique training opportunities for student facilitators.

Intervening on high-risk responses during ecological momentary assessment of suicidal thoughts: Is there an effect on study data?

Ecological momentary assessment (EMA) is increasingly used to study suicidal thoughts and behaviors (STBs). There is a potential ethical obligation for researchers to intervene when receiving information about suicidal thoughts in real time. A possible concern, however, is that intervening when receiving responses that indicate high risk for suicide during EMA research may impact how participants respond to questions about suicidal thoughts and thus affect the validity and integrity of collected data. We leveraged data from a study of adults and adolescents (N = 434) recruited during a hospital visit for STBs to examine whether monitoring and intervening on high-risk responses affects subsequent participant responding. Overall, we found mixed support for the notion that intervening on high-risk responses influences participants' ratings. Although we observed some evidence of discontinuity in subsequent responses at the threshold used to trigger response-contingent interventions, it was not clear that such discontinuity was caused by the interventions; lower subsequent responses could be due to effective intervention, participant desire to not be contacted again, or regression to the mean. Importantly, the likelihood of completing surveys did not change from before to after response-contingent intervention. Adolescents were significantly more likely than adults, however, to change their initial suicidal intent ratings from above to below the high-risk threshold after viewing automated response-contingent pop-up messages. Studies explicitly designed to assess the potential impact of intervening on high-risk responses in real-time monitoring research are needed, as this will inform effective, scalable strategies for intervening during moments of high suicide risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Latent Profile Analysis of Urges for Suicidal and Self-Harming Behavior: The Role of Social Support Instability.

College counseling centers are seeing increasing rates of suicidal thoughts and behaviors, and nonsuicidal self-injury (NSSI) among students. The high degree of need and limited resources underscores the need for novel approaches to identify at-risk students. We used latent profile analysis (LPA) to identify subgroups of students (n = 371) with different patterns of interpersonal factors and examine whether these subgroups differed by risk for self-injurious thoughts and behaviors. The best-fitting LPA had three profiles, which differed in urges to die by suicide and engage in NSSI. The profile with low average social support and higher instability (greater day-to-day fluctuations of self-reported social support and rejection) was associated with greatest risk, suggesting that this combination leaves individuals more vulnerable to suicide and NSSI.

Helpful for all? Examining the effects of psychotherapy treatment history on outcomes of single session, transdiagnostic cognitive behavioral interventions for university students.

Brief, transdiagnostic interventions are an efficient form of mental health care for resource-limited settings like universities. Little research, however, has examined for whom these treatments are most effective. One important factor may be psychotherapy treatment history. Here, we evaluate if treatment history influences the effects of a single-session cognitive behavioral group intervention with optional digital follow-up support across two independent, university-based studies. Undergraduate (N = 143) and graduate (N = 51) students reported their psychotherapy treatment history and completed self-report measures of emotional health before and approximately 1-month following the intervention. Across both samples, psychotherapy treatment history did not moderate changes in depression, anxiety, or emotional avoidance following the intervention. However, participants who were currently receiving psychotherapy began the workshop with lower coping self-efficacy than peers with no prior psychotherapy and saw larger gains in coping self-efficacy at follow-up. Results suggest that regardless of whether a student has previously received psychotherapy, they may benefit from brief, group transdiagnostic interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Suicidal thinking as affect regulation.

Nine percent of people worldwide report thinking about suicide at some point during their lives. A fundamental question we currently lack a clear answer to is: why do suicidal thoughts persist over time? One possibility is that suicidal thoughts serve adaptive functions for people who experience them. We tested whether suicidal thinking may serve as a form of affect regulation. In a real-time monitoring study among adults with recent suicidal thoughts (N = 105), we found that participants often endorsed using suicidal thinking as a form of affect regulation. The occurrence of suicidal thinking was followed by decreased negative affect. However, when assessing the direction of the relationship between suicidal thinking and negative affect, we also found positive bidirectional associations between them. Finally, using suicidal thinking as a form of affect regulation predicted the frequency and severity of suicidal thinking at later time points. These findings may help explain the persistence of suicidal thoughts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Real-time digital monitoring of a suicide attempt by a hospital patient.

Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before, during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention efforts.

A Pilot Implementation-Effectiveness Trial of a Single-Session Telehealth Workshop and Smartphone-Based Cognitive Behavioral Intervention for Managing Emotions Among College Students.

The number of college students who need mental health treatment outpaces the resources available to counseling centers to provide these needed services, presenting a need for low-cost, scalable interventions for college populations. We conducted a pilot implementation-effectiveness trial of a scalable treatment package that consisted of a single (telehealth) workshop plus a companion app that provided ecological momentary intervention. Participants (n = 177) received a workshop provided by counseling center staff and trainees. We were interested in (1) engagement with the app, (2) acceptability of the treatment package, and (3) initial effectiveness of the treatment package. Regarding engagement with the app, we found that participants preferred two reminder prompts per day and identified two key breakpoints when engagement declined significantly: at day 15, when just over half of the sample practiced a skill on the app at least once during the day and at day 41, when just over one third of people practiced a skill on the app each day. Regarding acceptability of the treatment package, students generally reported positive attitudes about the single-session workshop and app, but also noted that the content and assessments in the app needed to be more dynamic to improve how engaging it is. Regarding effectiveness, we found that about 75% of the sample experienced a significant reduction in negative affect from pre- to post-ecological momentary intervention. Moreover, there were significant pre- to post-study decreases in experiential avoidance and symptoms of anxiety and depression and increases in self-efficacy for managing negative emotions. The results of this study are promising in terms of providing initial support for this novel treatment package and provide useful information for researchers planning to develop and test similar interventions.

Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions.

The suicide rate (currently 14 per 100,000) has barely changed in the United States over the past 100 years. There is a need for new ways of preventing suicide. Further, research has revealed that suicidal thoughts and behaviors and the factors that drive them are dynamic, heterogeneous, and interactive. Most existing interventions for suicidal thoughts and behaviors are infrequent, not accessible when most needed, and not systematically tailored to the person using their own data (e.g., from their own smartphone). Advances in technology offer an opportunity to develop new interventions that may better match the dynamic, heterogeneous, and interactive nature of suicidal thoughts and behaviors. Just-In-Time Adaptive Interventions (JITAIs), which use smartphones and wearables, are designed to provide the right type of support at the right time by adapting to changes in internal states and external contexts, offering a promising pathway toward more effective suicide prevention. In this review, we highlight the potential of JITAIs for suicide prevention, challenges ahead (e.g., measurement, ethics), and possible solutions to these challenges.

The Effectiveness of a Brief Telehealth and Smartphone Intervention for College Students Receiving Traditional Therapy: Longitudinal Study Using Ecological Momentary Assessment Data.

BACKGROUND: Brief interventions such as mental health apps and single-session interventions are increasingly popular, efficacious, and accessible delivery formats that may be beneficial for college students whose mental health needs may not be adequately met by college counseling centers. However, no studies so far have examined the effectiveness of these modes of treatment for college students who are already receiving traditional therapy, despite it being common among this population. OBJECTIVE: The aim of this study was to compare the differences in self-reported momentary negative affect between college students in therapy and not in therapy who received a brief single-session intervention delivered by counseling center staff and a supplemental mobile app. METHODS: Data for this study were drawn from E-Manage, a brief mobile health intervention geared toward college students. Participants in the study were 173 college students who indicated whether they had received therapy. We conducted a multilevel model to determine whether there were differences between those in therapy versus not in therapy in negative affect reported throughout the study. Following this, we conducted multilevel models with therapy status as the predictor and negative affect as the outcome. RESULTS: Results of the multilevel model testing showed that the cross-level interaction between the time point (ie, pre- vs postexercise) and therapy status was significant (P=.008), with the reduction in negative affect from pre- to postexercise greater for those in therapy (b=-0.65, 95% CI -0.91 to -0.40; P<.001) than it was for those not in therapy (b=-0.31, 95% CI -0.43 to -0.19; P<.001). Therapy status was unassociated with both the pre-exercise (b=-1.69, 95% CI -3.51 to 0.13; P=.07) and postexercise (b=-1.37, 95% CI -3.17 to 0.43; P=.14) ratings of negative affect. CONCLUSIONS: These findings suggest that app-based and single-session interventions are also appropriate to use among college students who are receiving traditional therapy. A randomized controlled trial comparing students receiving therapy to students receiving therapy and E-Manage will be necessary to determine to what extent E-Manage contributed to the reductions in negative affect that therapy-attending college students experienced.

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers.

BACKGROUND: Interest in developing machine learning models that use electronic health record data to predict patients' risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk-prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. OBJECTIVE: The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk-prediction models in clinical practice. The specific goals are to better understand hospital providers' current practices for assessing and managing suicide risk; determine providers' perspectives on using automated suicide risk-prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. METHODS: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. RESULTS: Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers' general attitudes toward the practical use of automated suicide risk-prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. CONCLUSIONS: Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning-based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

An Ecological Momentary Intervention Study of Emotional Responses to Smartphone-Prompted CBT Skills Practice and the Relationship to Clinical Outcomes.

The practice of therapeutic skills outside of sessions in which they are learned is one presumed key component of cognitive behavioral therapy (CBT). Yet, our understanding of how skills practice relates to clinical outcomes remains limited. Here, we explored patients' emotional responses to CBT skills practice in a pilot study pairing smartphone-app-delivered skills reminders and guided practice (ecological momentary intervention [EMI]) using ecological momentary assessment (EMA). Participants (n = 25) were adults recently hospitalized for a suicide attempt or severe suicidal thinking. They received brief inpatient CBT (1 to 3 sessions covering core CBT skills from the Unified Protocol), followed by 1 month of EMI and EMA after discharge. On average, participants reported modest reductions in negative affect after skills use (i.e., immediate responses; median time elapsed = 4.30 minutes). Additionally, participants tended to report less negative affect when the timepoint preceding the current assessment included EMI skills practice, rather than EMA alone (i.e., delayed responses; median time elapsed between prompts = 2.17 hours). Immediate effects were unrelated to longer-term clinical outcomes, whereas greater delayed effects were associated with lower symptom severity at follow-up. Future studies should further examine how CBT skills use in daily life may alleviate symptoms.

Aggregating heart rate variability indices across sleep stage epochs ignores significant variance through the night.

BACKGROUND: Heart rate variability (HRV) is a widely utilized biomarker of autonomic regulatory functioning, and concomitant health and pathological states. A growing body of work is exploring HRV under sleeping conditions. Most of this literature utilizes either averaged HRV indices calculated from multiple sleep stage epochs, or averaged HRV throughout the night. Both approaches implicitly assume that HRV within sleep epoch types is consistent throughout the night. Given the robust literature indicating the existence of an endogenous cardiovascular circadian rhythm as well as the potential for effects for cumulative time asleep, we hypothesized that HRV would vary across distinct sleep epochs. METHODS: Participants underwent at least one night of home polysomnography that included electroencephalogram, electromyogram, and electrocardiogram (N = 73). All rapid eye movement (REM) and non-REM stage 2 (N2) sleep epochs with a duration greater than or equal to 5 min were identified for HRV analysis. Time and frequency domain indices of HRV were calculated for each sleep stage epoch. Linear mixed models were used to examine main effects of time on HRV indices for N2 and REM sleeps epochs respectively. RESULTS: Main effects of time were observed for all models. Patterns emerged for both the N2 and REM epochs, suggesting HRV indices are non-stationary (ie variable) across distinct sleep epochs through the course of the night. CONCLUSIONS: The present findings indicate HRV is non-stationary across sleep stage epochs. Aggregating HRV indices across sleep stage epochs likely obscures important transient effects and increases risk of type-I and type-II errors.

Can passive measurement of physiological distress help better predict suicidal thinking?

There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting-and ultimately, preventing-acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

Practices for monitoring and responding to incoming data on self-injurious thoughts and behaviors in intensive longitudinal studies: A systematic review.

Advancements in the understanding and prevention of self-injurious thoughts and behaviors (SITBs) are urgently needed. Intensive longitudinal data collection methods-such as ecological momentary assessment-capture fine-grained, "real-world" information about SITBs as they occur and thus have the potential to narrow this gap. However, collecting real-time data on SITBs presents complex ethical and practical considerations, including about whether and how to monitor and respond to incoming information about SITBs from suicidal or self-injuring individuals during the study. We conducted a systematic review of protocols for monitoring and responding to incoming data in previous and ongoing intensive longitudinal studies of SITBs. Across the 61 included unique studies/samples, there was no clear most common approach to managing these ethical and safety considerations. For example, studies were fairly evenly split between either using automated notifications triggered by specific survey responses (e.g., indicating current suicide risk) or monitoring and intervening upon (generally with a phone-based risk assessment) incoming responses (36%), using both automated notifications and monitoring/intervening (35%), or neither using automated notifications nor monitoring/intervening (29%). Certain study characteristics appeared to influence the safety practices used. Future research that systematically evaluates optimal, feasible strategies for managing risk in real-time monitoring research on SITBs is needed.

Skill Acquisition During Transdiagnostic Treatment With the Unified Protocol.

The Unified Protocol (UP) for Transdiagnostic Treatment of Emotional Disorders is an emotion-focused, cognitive-behavioral intervention developed to address the full range of anxiety, depressive, and related disorders. The UP consists of core therapeutic skills that, though unique in focus, are each designed to promote an approach-oriented stance toward emotional experiences. The goal of the present investigation was to characterize changes in these skills for patients that received a course of treatment with the UP, as well as to examine associations between skills and symptoms changes. Patients with principal anxiety disorders, assigned to receive treatment with the UP (N = 88) as part of a randomized controlled trial, were included in this study. They completed validated self-report measures of UP skills (Understanding Emotions, Mindful Emotion Awareness, Cognitive Flexibility, Countering Emotional Behaviors, and Interoceptive Awareness and Tolerance), as well as clinician-rated measures of psychological symptoms. Skill measures improved significantly over the course of 12 to 16 UP treatment sessions and changes in these skills measures were associated with improvements in anxiety symptoms. Determining whether improvement on all the skills learned during a course of treatment with UP is associated with symptom remission is critical to establishing the most streamlined and efficient interventions that may ultimately be best suited to widespread dissemination.

The Nature, Timing, and Symptom Trajectories of Dropout From Transdiagnostic and Single-Diagnosis Cognitive-Behavioral Therapy for Anxiety Disorders.

Dropout from psychotherapy is common and can have negative effects for patients, providers, and researchers. A better understanding of when and why patients stop treatment early, as well as actionable factors contributing to dropout, has the potential to prevent it. Here, we examined dropout from a large randomized controlled trial of transdiagnostic versus single-diagnosis cognitive-behavioral treatment (CBT) for patients with anxiety disorders (n = 179; Barlow et al., 2017). We aimed to characterize the timing of and reasons for dropout and test whether participants who dropped out had different symptom trajectories than those who completed treatment. Results indicated that overall, the greatest risk of dropout was prior to the first treatment session. In single-diagnosis CBT, dropout risk was particularly elevated before the first session and after other early sessions, whereas in transdiagnostic CBT, dropout risk was low and stable before and during treatment. Participants most often dropped out due to failure to comply with study procedures or dissatisfaction with or desiring alternative treatment. Results from multilevel models showed that trajectories of anxiety symptoms did not significantly differ between dropouts and completers. These findings suggest that there may be specific time windows for targeted and timely interventions to prevent dropout from CBT.