Using Progress Feedback to Enhance Treatment Outcomes: A Narrative Review.

We face increasing demand for greater access to effective routine mental health services, including telehealth. However, treatment outcomes in routine clinical practice are only about half the size of those reported in controlled trials. Progress feedback, defined as the ongoing monitoring of patients' treatment response with standardized measures, is an evidence-based practice that continues to be under-utilized in routine care. The aim of the current review is to provide a summary of the current evidence base for the use of progress feedback, its mechanisms of action and considerations for successful implementation. We reviewed ten available meta-analyses, which report small to medium overall effect sizes. The results suggest that adding feedback to a wide range of psychological and psychiatric interventions (ranging from primary care to hospitalization and crisis care) tends to enhance the effectiveness of these interventions. The strongest evidence is for patients with common mental health problems compared to those with very severe disorders. Effect sizes for not-on-track cases, a subgroup of cases that are not progressing well, are found to be somewhat stronger, especially when clinical support tools are added to the feedback. Systematic reviews and recent studies suggest potential mechanisms of action for progress feedback include focusing the clinician's attention, altering clinician expectations, providing new information, and enhancing patient-centered communication. Promising approaches to strengthen progress feedback interventions include advanced systems with signaling technology, clinical problem-solving tools, and a broader spectrum of outcome and progress measures. An overview of methodological and implementation challenges is provided, as well as suggestions for addressing these issues in future studies. We conclude that while feedback has modest effects, it is a small and affordable intervention that can potentially improve outcomes in psychological interventions. Further research into mechanisms of action and effective implementation strategies is needed.

The temporal order of emotional, cognitive, and behavioral gains in daily life during treatment of depression.

OBJECTIVE: Despite the importance for understanding mechanisms of change, little is known about the order of change in daily life emotions, cognitions, and behaviors during treatment of depression. This study examined the within-person temporal order of emotional, cognitive, and behavioral improvements using ecological momentary assessment data. METHOD: Thirty-two individuals with diagnosed depression completed ecological momentary assessment questions on emotions (sad mood, happy mood), behaviors (social interaction, number of activities), and cognitive variables (worrying, negative self-thoughts) 5 times a day during a 4-month period in which they underwent psychotherapy for depression. Nonparametric change-point analyses were used to determine the timing of gains (i.e., improvements in the mean of each variable) for each individual. We then established whether the first (i.e., earliest) gains in emotions preceded, followed, or occurred in the same week as cognitive and behavioral gains for each individual. RESULTS: Contrary to our hypotheses, first gains in behaviors did not precede first emotional gains (3 times, 8%) more often than they followed them (26 times, 70%). Cognitive gains often occurred in the same week as first emotional gains (43 times, 58%) and less often preceded (13 times, 18%) or followed emotional gains (18 times, 24%). CONCLUSION: The first improvements in behaviors did not tend to precede the first improvements in emotions likely because fewer behavioral gains were found. The finding that cognitive variables tend to improve around the same time as sad mood may explain why many studies failed to find that cognitive change predicts later change in depressive symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling.

We aim to use topic modeling, an approach for discovering clusters of related words ("topics"), to predict symptom severity and therapeutic alliance in psychotherapy transcripts, while also identifying the most important topics and overarching themes for prediction. We analyzed 552 psychotherapy transcripts from 124 patients. Using BERTopic (Grootendorst, 2022), we extracted 250 topics each for patient and therapist speech. These topics were used to predict symptom severity and alliance with various competing machine-learning methods. Sensitivity analyses were calculated for a model based on 50 topics, LDA-based topic modeling, and a bigram model. Additionally, we grouped topics into themes using qualitative analysis and identified key topics and themes with eXplainable Artificial Intelligence (XAI). Symptom severity could be predicted with highest accuracy by patient topics ( r =0.45, 95%-CI 0.40, 0.51), whereas alliance was better predicted by therapist topics ( r =0.20, 95%-CI 0.16, 0.24). Drivers for symptom severity were themes related to health and negative experiences. Lower alliance was correlated with various themes, especially psychotherapy framework, income, and everyday life. This analysis shows the potential of using topic modeling in psychotherapy research allowing to predict several treatment-relevant metrics with reasonable accuracy. Further, the use of XAI allows for an analysis of the individual predictive value of topics and themes. Limitations entail heterogeneity across different topic modeling hyperparameters and a relatively small sample size.

The Effects of Switching to Video Therapy on In-Session Processes in Psychotherapy During the COVID-19 Pandemic.

OBJECTIVE AND AIM: This study aimed to assess the impact of switching from face-to-face (f2f) psychotherapy to video therapy (VT) due to the COVID-19 pandemic on in-session processes, i.e., the therapeutic alliance, coping skills, and emotional involvement, as rated by both patients and therapists. METHODS: A total of N = 454 patients with mood or anxiety disorders were examined. The intervention group (IG) consisted of n = 227 patient-therapist dyads, who switched from f2f to VT, while the control group (CG) consisted of n = 227 patient-therapist dyads, who were treated f2f before the pandemic. To evaluate the effects of switching to VT on in-session processes, three longitudinal piecewise multilevel models, one per process variable, were fitted. Each process variable was regressed on the session number with a slope for the three sessions before switching to VT and a second slope for up to six VT sessions afterwards. RESULTS: The therapeutic alliance significantly increased after switching from f2f to VT across the two groups (IG and CG) and raters (patients and therapists) with no differences between IG and CG. On average, patients rated the therapeutic alliance better than therapists. Coping skills significantly increased after switching from f2f to VT across the two groups and raters, but the CG rated coping skills higher than the IG after the switch. Overall, therapists rated coping skills higher than patients. Emotional involvement did not significantly increase after switching to VT across the two groups and raters and there was no significant difference between patient and therapist ratings. DISCUSSION: In conclusion, the switch to VT had no negative impact on the therapeutic alliance and emotional involvement. However, more coping skills were reported in the CG than in the IG after the switch to VT, which was mainly due to a stagnation in patient-rated coping skills in the IG.

Routine Outcome Monitoring and Clinical Feedback in Psychotherapy: Recent Advances and Future Directions.

In the past decade, there has been an increase in research related to the routine collection and active use of standardized patient data in psychotherapy. Research has increasingly focused on personalization of care to patients, clinical skills and interventions that modulate treatment outcomes, and implementation strategies, all of which appear to enhance the beneficial effects of ROM and feedback. In this article, we summarize trends and recent advances in the research on this topic and identify several essential directions for the field in the short to medium term. We anticipate a broadening of research from the focus on average effects to greater specificity around what kinds of feedback, provided at what time, to which individuals, in what settings, are most beneficial. We also propose that the field needs to focus on issues of health equity, ensuring that ROM can be a vehicle for increased wellbeing for those who need it most. The complexity of mental healthcare systems means that there may be multiple viable measurement solutions with varying costs and benefits to diverse stakeholders in different treatment contexts, and research is needed to identify the most influential components in each of these contexts.

Symptom Reduction and Engagement in a Cognitive-Behavioral Mobile Phone App: A Study of User Profiling to Determine Prognostic Indicators.

OBJECTIVE: We investigated the presence of latent transition profiles in a sample of users of a cognitive-behavioral mental health app for the general population. Users' baseline characteristics were used as predictors of the profiles. The role of engagement with the app in the transition profiles was examined. METHOD: A total of 541 users completed the Patient Health Questionnaire-9 and the General Anxiety Disorder-7 when started using the app and 30 days after. Random-Intercept Latent Transition Analysis was implemented to identify users' profiles and transition patterns as classes. The age of the users and the Emotional Self-Awareness Scale-Revised (ESAS-R) were used as predictors of class membership at baseline. The Homework Rating Scale-Mobile Application (HRS-MA; as a measure of engagement) was used as a predictor of class membership at 30 days of app use. RESULTS: A 3-class solution was obtained according to the severity of symptoms (from mild to moderately severe). Age and ESAS-R predicted class membership initially; the higher the age and ESAS-R, the higher the probability of starting using the app with lower distress levels. The HRS-MA predicted class membership at 30 days of app use; the higher the engagement for more symptomatic and younger users, the higher the probability of improvement. However, older users tended to engage less. CONCLUSION: Our findings underpin the relevance of easily accessible digital interventions for young adults with mild to moderate mental health problems. Further studies and developments are required to enhance these apps for older cohorts.

Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy.

OBJECTIVE: Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD: We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS: The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION: Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.

Where it all begins: Predicting initial therapeutic skills before clinical training in cognitive behavior therapy.

To train novice students adequately, it is crucial to understand where they start and how they develop their skills. This study examined the impact of novice students' characteristics on their initial clinical micro-skills when treating simulated patients with cognitive behavior therapy. The sample consisted of 44 graduate psychology students treating seven simulated patients. Clinical micro-skills were measured both using video-based ratings in reaction to short video clips of simulated patients (via the Facilitative Interpersonal Skills (FIS) performance task) and by using video-based ratings within a session with a simulated patient (using the Inventory of Therapeutic Interventions and Skills; ITIS). Two separate LASSO regressions were performed using machine learning to select potential predictors for both skills assessments. Subsequently, a bootstrapping algorithm with 10,000 iterations was used to examine the variability of regression coefficients. Using LASSO regression, we identified two predictors for clinical micro-skills in standardized scenarios: extraversion (b = 0.10) and resilience (b = 0.09), both were not significantly associated with clinical micro-skills. Together, they explained 15% of the skill variation. Bootstrapping confirmed the stability of these predictors. For clinical micro-skills in sessions, only competitiveness was excluded by LASSO regression, and all predictors showed significant instability. The results provide initial evidence that trainees' resilience and extraversion should be promoted in the clinical training of cognitive behavior therapy. More studies on clinical micro-skills and training with larger sample sizes are needed to fully understand clinical development.

Treatment credibility as a mechanism of change in cognitive behavioral therapy: Effects on depression and anxiety.

OBJECTIVE: This study aimed to provide evidence for treatment credibility (TC) as a potential mechanism of change in cognitive behavioral therapy (CBT). Therefore, it focused on within-person effects that are free of the influence of stable characteristics and thus allow to exclude certain alternative explanations for the association under study. METHOD: The sample included 1,423 patients receiving outpatient CBT, who presented a wide variety of psychiatric diagnoses (mostly affective and anxiety disorders). TC, depression, and anxiety were measured every fifth session from Session 5 to 25 using the Credibility Expectancy Questionnaire (CEQ), the Patient Health Questionnaire-9 (PHQ-9), and the General Anxiety Disorder-7 (GAD-7), respectively. Symptom severity was assessed every session using the Hopkins Symptom Checklist-11. Within- and between-person effects of TC, depression, and anxiety were analyzed using the latent curve model with structured residuals (LCM-SRs). In exploratory analyses, within-person effects of TC on next-session symptom severity were assessed using a modification of the LCM-SR. RESULTS: LCM-SRs exhibited excellent fit in main analyses. There were significant negative correlations of both intercepts and slopes (between-person level) of CEQ and PHQ-9 as well GAD-7. No significant cross-lagged effects (within-person level) were found over the five-session interval. However, session-wise analyses revealed significant cross-lagged effects of CEQ on Hopkins Symptom Checklist-11. CONCLUSIONS: This study is the first to find significant within-person effects of TC in session-wise analyses. This lends preliminary support to the notion of TC as a mechanism of change. The lack of significant findings at the five-session interval is discussed considering the specific design used in this study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An investigation of treatment return after psychological therapy for depression and anxiety.

BACKGROUND: Some patients return for further psychological treatment in routine services, although it is unclear how common this is, as scarce research is available on this topic. AIMS: To estimate the treatment return rate and describe the clinical characteristics of patients who return for anxiety and depression treatment. METHOD: A large dataset (N=21,029) of routinely collected clinical data (2010-2015) from an English psychological therapy service was analysed using descriptive statistics. RESULTS: The return rate for at least one additional treatment episode within 1-5 years was 13.7%. Furthermore, 14.5% of the total sessions provided by the service were delivered to treatment-returning patients. Of those who returned, 58.0% continued to show clinically significant depression and/or anxiety symptoms at the end of their first treatment, while 32.0% had experienced a demonstrable relapse before their second treatment. CONCLUSIONS: This study estimates that approximately one in seven patients return to the same service for additional psychological treatment within 1-5 years. Multiple factors may influence the need for additional treatment, and this may have a major impact on service activity. Future research needs to further explore and better determine the characteristics of treatment returners, prioritise enhancement of first treatment recovery, and evaluate relapse prevention interventions.

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

OBJECTIVE: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. METHOD: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. RESULTS: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. CONCLUSION: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Applying precision methods to treatment selection for moderate/severe depression in person-centered experiential therapy or cognitive behavioral therapy.

OBJECTIVE: To develop two prediction algorithms recommending person-centered experiential therapy (PCET) or cognitive-behavioral therapy (CBT) for patients with depression: (1) a full data model using multiple trial-based and routine variables, and (2) a routine data model using only variables available in the English NHS Talking Therapies program. METHOD: Data was used from the PRaCTICED trial comparing PCET vs. CBT for 255 patients meeting a diagnosis of moderate or severe depression. Separate full and routine data models were derived and the latter tested in an external data sample. RESULTS: The full data model provided the better prediction, yielding a significant difference in outcome between patients receiving their optimal vs. non-optimal treatment at 6- (Cohen's d = .65 [.40, .91]) and 12 months (d = .85 [.59, 1.10]) post-randomization. The routine data model performed similarly in the training and test samples with non-significant effect sizes, d = .19 [-.05, .44] and d = .21 [-.00, .43], respectively. For patients with the strongest treatment matching (d ≥ 0.3), the resulting effect size was significant, d = .38 [.11, 64]. CONCLUSION: A treatment selection algorithm might be used to recommend PCET or CBT. Although the overall effects were small, targeted matching yielded somewhat larger effects.

A Clinical Leadership Lens on Implementing Progress Feedback in Three Countries: Development of a Multidimensional Qualitative Coding Scheme.

BACKGROUND: Progress feedback, also known as measurement-based care (MBC), is the routine collection of patient-reported measures to monitor treatment progress and inform clinical decision-making. Although a key ingredient to improving mental health care, sustained use of progress feedback is poor. Integration into everyday workflow is challenging, impacted by a complex interrelated set of factors across patient, clinician, organizational, and health system levels. This study describes the development of a qualitative coding scheme for progress feedback implementation that accounts for the dynamic nature of barriers and facilitators across multiple levels of use in mental health settings. Such a coding scheme may help promote a common language for researchers and implementers to better identify barriers that need to be addressed, as well as facilitators that could be supported in different settings and contexts. METHODS: Clinical staff, managers, and leaders from two Dutch, three Norwegian, and four mental health organizations in the USA participated in semi-structured interviews on how intra- and extra-organizational characteristics interact to influence the use of progress feedback in clinical practice, supervision, and program improvement. Interviews were conducted in the local language, then translated to English prior to qualitative coding. RESULTS: A team-based consensus coding approach was used to refine an a priori expert-informed and literature-based qualitative scheme to incorporate new understandings and constructs as they emerged. First, this hermeneutic approach resulted in a multi-level coding scheme with nine superordinate categories and 30 subcategories. Second-order axial coding established contextually sensitive categories for barriers and facilitators. CONCLUSIONS: The primary outcome is an empirically derived multi-level qualitative coding scheme that can be used in progress feedback implementation research and development. It can be applied across contexts and settings, with expectations for ongoing refinement. Suggestions for future research and application in practice settings are provided. Supplementary materials include the coding scheme and a detailed playbook.

Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research.

Mental health services are experiencing notable transformations as innovative technologies and artificial intelligence (AI) are increasingly utilized in a growing number of studies and services.These cutting-edge technologies carry the promise of substantial improvements in the field of mental health. Nevertheless, questions emerge about the alignment of novel technologies and AI systems with human needs, especially in the context of vulnerable populations receiving mental healthcare. The practice-oriented research (POR) model is pivotal in seamlessly integrating these emerging technologies into clinical research and practice. It underscores the importance of tight collaboration between clinicians and researchers, all driven by the central goal of ensuring and elevating client well-being. This paper focuses on how novel technologies can enhance the POR model and highlights its pivotal role in integrating these technologies into clinical research and practice. We discuss two key phases: pre-treatment, and during treatment. For each phase, we describe the challenges, present the major technological innovations, describe recent studies exemplifying technology use, and suggest future directions. Ethical concerns and the importance of aligning humans and technology are also considered, in addition to implications for practice and training.

Using machine learning algorithms to predict the effects of change processes in psychotherapy: Toward process-level treatment personalization.

This study aimed to develop and test algorithms to determine the individual relevance of two psychotherapeutic change processes (i.e., mastery and clarification) for outcome prediction. We measured process and outcome variables in a naturalistic outpatient sample treated with an integrative treatment for a variety of diagnoses (n = 608) during the first 10 sessions. We estimated individual within-patient effects of each therapist-evaluated process of change on patient-evaluated subsequent outcomes on a session-by-session basis. Using patients' baseline characteristics, we trained machine learning algorithms on a randomly selected subsample (n = 407) to predict the effects of patients' process variables on outcome. We subsequently tested the predictive capacity of the best algorithm for each process on a holdout subsample (n = 201). We found significant within-patient effects of therapist perceived mastery and clarification on subsequent outcome. In the holdout subsample, the best-performing algorithms resulted in significant but small-to-medium correlations between the predicted and observed relevance of therapist perceived mastery (r = .18) and clarification (r = .16). Using the algorithms to create criteria for individual recommendations, in the holdout sample, we identified patients for whom mastery (14%) or clarification (18%) were indicated. In the mastery-indicated group, a greater focus on mastery was moderately associated with better outcome (r = .33, d = .70), while in the clarification-indicated group, the focus was not related to outcome (r = -.05, d = .10). Results support the feasibility of performing individual predictions regarding mastery process relevance that can be useful for therapist feedback and treatment recommendations. However, results will need to be replicated with prospective experimental designs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling.

Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?).A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics.There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes.Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.

On the current psychotherapeutic situation for persons with pornography use disorder in Germany.

Background and aims: For the first time, the ICD-11 provides the diagnosis compulsive sexual behavior disorder (CSBD) that can be assigned for pornography use disorder (PUD). This study aimed to estimate the prevalence of PUD and associated consequences in Germany, to identify the psychotherapy demand among likely PUD (lPUD) cases and the treatment supply in different psychotherapeutic settings, to survey psychotherapists' level of expertise regarding PUD, and to identify predictors for psychotherapy demand. Methods: Four studies were conducted: 1. Online study in the general population (n = 2070; m = 48.9%, f = 50.8%, d = 0.2%), 2. Survey among practicing psychotherapists (n = 983), 3. Survey of psychotherapists in psychotherapeutic outpatient clinics (n = 185), 4. Interviews with psychotherapeutic inpatient clinics (n = 28). Results: The estimated prevalence of lPUD in the online study was 4.7% and men were 6.3 times more often affected than women. Compared to individuals without PUD, individuals with lPUD more often indicated negative consequences in performance-related areas. Among lPUD cases, 51.2% of men and 64.3% of women were interested in a specialized PUD treatment. Psychotherapists reported 1.2%-2.9% of lPUD cases among their patients. 43.2%-61.5% of psychotherapists stated to be poorly informed about PUD. Only 7% of psychotherapeutic inpatient clinics provided specific treatments to patients with PUD. While, among other factors, negative consequences attributed to lPUD were predictive for psychotherapy demand, weekly pornography consumption, subjective well-being, and religious attachment were not. Discussion and conclusions: Although PUD occurs quite often in Germany, availability of mental health care services for PUD is poor. Specific PUD treatments are urgently needed.