Measurement-Based and Data-Informed Psychological Therapy

Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis

ABSTRACT

This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. However, empirical evidence that personalization of psychological treatments can optimize treatment outcomes in CPP is needed. This study seeks to estimate heterogeneity of treatment effect for cognitive behavioral therapy (CBT) as the psychological treatment approach for CPP with the greatest evidence base. For this purpose, a Bayesian variance ratio meta-regression is conducted using updated data from 2 recently published meta-analyses with randomized controlled trials comparing CBT delivered face-to-face to treatment-as-usual or waiting list controls. Heterogeneity in patients with CPP would be reflected by a larger overall variance in the post-treatment score compared with the control group. We found first evidence for an individual treatment effect in CBT compared with the control group. The estimate for the intercept was 0.06, indicating a 6% higher variance of end point values in the intervention groups. However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP.A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.

Daily stress reactivity and risk appraisal mediates childhood parental abuse predicting adulthood psychopathology severity: An 18-year longitudinal mediation analysis

Identifying mechanisms of childhood abuse-adulthood psychopathology relations could facilitate preventive efforts, but most prior studies used cross-sectional or two-wave designs and did not test the effects of childhood maternal and paternal abuse separately. Our 18-year three-wave study thus determined if Wave 2 daily stress reactivity and risk appraisal severity mediated Wave 1 retrospectively-reported childhood maternal and paternal abuse on Wave 3 generalized anxiety disorder (GAD), major depressive disorder (MDD), panic disorder (PD), alcohol (AUD), and substance use disorder (SUD) self-rated symptom severity. Longitudinal structural equation modeling was employed, adjusting for Wave 1 psychopathology severity. Higher childhood maternal and paternal abuse consistently predicted greater future daily stress reactivity and risk appraisal, and these mediators subsequently predicted increased GAD, MDD, and PD, but not AUD and SUD severity. Daily stress reactivity and risk appraisal consistently mediated the pathways between childhood maternal and paternal abuse predicting heightened adulthood GAD, MDD, and PD (Cohen's d = 0.333-0.888) but not AUD and SUD severity. Mediation effect sizes were stronger for childhood maternal (24.5-83.0%) than paternal (19.5-56.0%) abuse as the predictor. The latent interaction between Wave 1 childhood maternal and paternal abuse did not moderate the effect of Wave 1 maternal or paternal abuse on any Wave 3 adulthood psychopathology severity through Wave 2 daily stress reactivity and risk appraisal. Our research emphasizes the urgent requirement for continuous evaluation and intervention initiatives in trauma-informed care, both in inpatient and outpatient treatment settings.

Intake Characteristics as Predictors of Psychotherapy Outcome in a Practice Research Network in Argentina

There are few studies exploring intake diagnostic characteristics as predictors of change in integrative naturalistic settings. The aim of this study is to explore baseline variables at the intake process and establish the predictive value of the individual trajectories of the patients. We recruited 259 patients undergoing an integrative psychotherapy network of psychotherapists from Buenos Aires, Argentina. Every therapist completed the intake form of each patient involved in the routine outcome monitoring. Thereafter step-wise regressions based on forward selection strategies were used, in order to identify meaningful baseline predictors of patients' clinical evolution, derived from the intake process. The selected predictors were social support network, subjective distress, the initial measure of clinical distress, unemployment, sociocultural status and reactance. When including those six variables in a multilevel model, the results indicate that social support network, subjective distress, and the initial measure of clinical distress were significant predictors of the trajectories of OQ-30, whereas unemployment, sociocultural status and reactance were not significant. The results regarding social support network are in line with the literature, while results of socioeconomic status (unemployment and sociocultural level) move in an opposite direction in comparison to the available evidence. Moreover, the mental health findings (initial OQ-30 and subjective distress) confirm the contradictory body of literature produced in this domain. Finally, reactance seems to be a significant predictor in previous study in contradiction of our results. Overall, this endeavor constitutes important but preliminary evidence to enhance the production of bottom-up science within practice research networks in the global south.

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.

Implementation of an Ecological Momentary Assessment (EMA) in Naturalistic Psychotherapy Settings: Qualitative Insights from Patients, Therapists, and Supervisors Perspectives

Ecological momentary assessment (EMA) allows measuring intra-individual processes moment by moment, identifying and modeling, in a naturalistic way, individual levels and changes in different psychological processes. However, active EMA requires a high degree of adherence, as it implies a significant burden for patients. Moreover, there is still no consensus on standardized procedures for implementation. There have been few results in detecting desirable characteristics for the design and implementation of an EMA device. Studies that address these issues from the perspectives of participants in psychotherapeutic processes are needed. To analyze the perspectives of patients, therapists and supervisors on the implementation of an EMA device in a psychotherapeutic treatment for depression. The sample will include eight patients, eleven therapists and five supervisors, taken from a research project that implemented an EMA system for monitoring the dynamics of affectivity at the beginning of psychotherapies for depression. Semi-structured interviews specific to each group are being conducted and analyzed from a qualitative approach based on consensual qualitative research (CQR). Participants reported having a positive evaluation of the study's informational resources and implementation. Difficulties were expressed in responding in the morning hours and the importance of having a customized EMA that is tailored to the needs of the patients was expressed. Furthermore, patients and therapists agreed that the impact of the use of the monitoring system on treatment was neutral or positive. In contrast, patients considered the EMA to be positive for their daily life.

The long shadow of early-change patterns: a 3-year follow-up after the use of a web-based intervention for mild to moderate depressive symptoms

Web-based interventions can be effective in treating depressive symptoms. Patients with risk not responding to treatment have been identified by early change patterns. This study aims to examine whether early changes are superior to baseline parameters in predicting long-term outcome. In a randomized clinical trial with 409 individuals experiencing mild to moderate depressive symptoms using the web-based intervention deprexis, three latent classes were identified (early response after registration, early response after screening and early deterioration) based on early change in the first four weeks of the intervention. Baseline variables and these classes were included in a Stepwise Cox Proportional Hazard Multiple Regression to identify predictors associated with the onset of remission over 36-months. Early change class was a significant predictor of remission over 36 months. Compared to early deterioration after screening, both early response after registration and after screening were associated with a higher likelihood of remission. In sensitivity and secondary analyses, only change class consistently emerged as a predictor of long-term outcome. Early improvement in depression symptoms predicted long-term outcome and those showing early improvement had a higher likelihood of long-term remission. These findings suggest that early changes might be a robust predictor for long-term outcome beyond baseline parameters.

Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.

A network analysis of mechanisms of change during exposures over the course of intensive OCD treatment

Exposure and response prevention (ERP) is an evidence-based treatment for obsessive-compulsive disorder (OCD). Theories for how it works vary in their emphasis on active mechanisms of change. The current study aimed to clarify mechanisms of change in ERP for OCD using network analysis, comparing ERP networks at the start and end of intensive treatment (partial hospital and residential). In our sample of 182 patients, the most central node in both networks was engagement with exposure, which was consistently related to greater understanding of ERP rationale, higher willingness, and less ritualization, accounting for all other variables in the network. There were no significant differences in networks between the start and end of treatment. These results suggest that nonspecific parameters like facilitating engagement in exposures without ritualizing and providing a clear rationale to clients may be key to effective treatment. As such, it may be useful for clinicians to spend adequate time underscoring the need to eliminate rituals to fully engage in exposure tasks and explaining the rationale for ERP prior to doing exposures, regardless of theoretical orientation. Nonetheless, findings represent group-level statistics and more fine-grained idiographic analyses may reveal individual-level differences with respect to central mechanisms of change. Other limitations include demographic homogeneity of our sample.

Work-Related Cognitive Behavioral Therapy for racially and economically diverse unemployed persons with social anxiety: A randomized clinical trial

Individuals with Social Anxiety Disorder (SAD) are at risk for employment problems. This multi-site trial examined the efficacy of Work-Related Cognitive Behavioral Therapy provided alongside vocational services as usual (WCBT+VSAU), a group-based treatment designed to improve mental health and employment outcomes for individuals with SAD. Vocational service-seeking participants with SAD (N = 250) were randomized to either WCBT+VSAU or VSAU-alone. Hypotheses were that participants randomized to WCBT+VSAU would report less social anxiety, less depression, and more hours worked than participants randomized to VSAU-alone. WCBT+VSAU participants had significantly greater improvements on the Liebowitz Social Anxiety Scale (LSAS; d=-.25, CI=-0.49 to -0.02, p = .03) at post-assessment compared to VSAU-alone. The conditions did not differ on any variable at later time points or on secondary outcomes. Unexpectedly, participants randomized to VSAU-alone experienced LSAS improvements, similar to WCBT+VASU at later timepoints. Baseline psychological flexibility (beta=-.098 [-0.19-0.008]) and depression (beta=-0.18 [-0.34-0.009]) moderated change in social anxiety. Participants with lower psychological flexibility and higher depression responded more strongly to WCBT+VSAU than VSAU-alone over the duration of the study, suggesting that WCBT+VSAU may particularly benefit those with greater psychopathology. Results indicate that vocational centers are promising settings for treating SAD and employment-focused refinements are likely needed to improve work outcomes.

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.

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.

Bridging the feedback implementation gap: A comparison of empirical and rational decision rules in naturalistic psychotherapy

Objective: Previous research indicates positive effects of feedback based on rational or empirical decision rules in psychotherapy. The implementation of these usually session-to-session-based feedback systems into clinical practice, however, remains challenging. This study aims to evaluate decision rules based on routine outcome monitoring with reduced assessment frequency. Method: Data routinely collected every 5-20 sessions of N = 3758 patients treated with CBT in an outpatient clinic (Msessions = 42.8, SD = 15.4) were used to develop feedback decision rules based on the expected treatment response and nearest neighbors approach, the reliable change index, and method of percental improvement. The detection of patients at risk of treatment failure served as primary endpoint. Results: Significantly lower reliable improvement, higher reliable deterioration rates, and smaller effect sizes were found for patients identified at risk of treatment failure by all rules. The nearest neighbors-based approach showed the highest sensitivity regarding the detection of reliably deteriorated cases. Conclusion: Consistent with previous research, the empirical models outperformed the rational rules. Still, the first-time used percental improvement-based rule also showed satisfactory results. Overall, the results point to the potential of basic feedback systems that might be easier to implement in practice than session-to-session based systems.

From Theory to Practice: A Transtheoretical Treatment and Training Model (4TM)

Background

In this paper, we present the conceptual background and clinical implications of a research-based transtheoretical treatment and training model (4TM).

Method

The model implements findings from psychotherapy outcome, process, and feedback research into a clinical and training framework that is open to future research.

Results

The framework is based on interventions targeting patient processes on a behavioral, cognitive, emotional, motivational, interpersonal, and systemic/socio-cultural level. The 4TM also includes a data-based decision support and feedback system called the Trier Treatment Navigator (TTN).

Conclusion

We discuss important problems associated with clinical orientations solely based on one school of thought. We then contrast these concerns with a clinical and training framework that embraces ongoing research, serving as a guiding structure for process-based transtheoretical interventions. Such research-based psychological therapy can take both traditional and novel clinical developments as well as findings from psychotherapy research into account and be adaptively disseminated to a variety of patient populations.

Psychotherapists' Experience with In-Session Use of Routine Outcome Monitoring: A Qualitative Meta-analysis

Routine outcome monitoring (ROM) has become an increasingly utilized tool in therapeutic practice that has the potential to improve therapy outcomes. This study aimed to synthesize the findings of existing qualitative studies investigating how clinicians use ROM in their work with clients. A systematic search of qualitative studies on clinicians' experience with the use of ROM in mental health services was conducted via PsycInfo, PsycArticles, Medline, Web of Science, and Scopus databases. Qualitative meta-analysis was used to synthesize the finding of the primary studies. Forty-seven studies met the inclusion criteria. The analysis resulted in 21 meta-categories organized into six clusters, namely (1) obtaining clinically relevant information, (2) adapting treatment, (3) facilitating communication, (4) enhancing the therapeutic relationship, (5) facilitating change in clients, and (6) personalized usage of ROM. The meta-analysis revealed that clinicians utilized ROM in diverse ways, including both informational and communicational functions. From the clinicians' perspective, ROM was an element that, on the one hand, introduced additional structure and standardization in treatment and, on the other hand, allowed for greater flexibility and tailoring of treatment.

Leveraging Single-Case Experimental Designs to Promote Personalized Psychological Treatment: Step-by-Step Implementation Protocol with Stakeholder Involvement of an Outpatient Clinic for Personalized Psychotherapy

Our objective is to implement a single-case experimental design (SCED) infrastructure in combination with experience-sampling methods (ESM) into the standard diagnostic procedure of a German outpatient research and training clinic. Building on the idea of routine outcome monitoring, the SCED infrastructure introduces intensive longitudinal data collection, individual effectiveness measures, and the opportunity for systematic manipulation to push personalization efforts further. It aims to empower psychotherapists and patients to evaluate their own treatment (idiographic perspective) and to enable researchers to analyze open questions of personalized psychotherapy (nomothetic perspective). Organized around the principles of agile research, we plan to develop, implement, and evaluate the SCED infrastructure in six successive studies with continuous stakeholder involvement: In the project development phase, the business model for the SCED infrastructure is developed that describes its vision in consideration of the context (Study 1). Also, the infrastructure's prototype is specified, encompassing the SCED procedure, ESM protocol, and ESM survey (Study 2 and 3). During the optimization phase, feasibility and acceptability are tested and the infrastructure is adapted accordingly (Study 4). The evaluation phase includes a pilot implementation study to assess implementation outcomes (Study 5), followed by actual implementation using a within-institution A-B design (Study 6). The sustainability phase involves continuous monitoring and improvement. We discuss to what extent the generated data could be used to address current questions of personalized psychotherapy research. Anticipated barriers and limitations during the implementation processes are outlined.

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.

Towards a taxonomy of mechanisms of change? Findings from an expert survey on the association between common factors and specific techniques in psychotherapy

OBJECTIVE

In the present study, we used structural equation modeling (SEM) to investigate the complex relationship between common factors, i.e., mechanisms of change, and specific factors, i.e., therapeutic techniques.

METHOD

N = 256 psychotherapy experts were asked to rate the appropriateness of 14 techniques commonly used in psychotherapy to facilitate five different common factors - resource activation, motivational clarification, self-management & emotion regulation, social competence, and therapeutic relationship. Using SEM, we defined techniques as indicators and common factors as latent variables. Data were split randomly into two subsets. Indicators were selected if three a priori defined criteria were met based on training data (n = 128). Subsequently, the goodness of model fit was assessed in the test data (n = 128).

RESULTS

The proposed model revealed adequate fit. All factor loadings were theoretically sound and significant in magnitude. Findings suggest that psychotherapy experts discriminate between common factors by their various associations with therapeutic techniques.

CONCLUSION

Suggestions are made, how therapeutic techniques are to be used to facilitate desirable change in the patient. Our model is a step towards a taxonomy of mechanisms of change that may help to improve research-informed decision-making.

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.

Individual-Specific Animated Profiles of Mental Health

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

[How Cooperation Instead of Coexistence in Psychotherapy Research can Improve Science, Practice and Continuing Education]

Die kontinuierliche Erhebung psychometrischer Daten vor, während und nach einer psychotherapeutischen Behandlung kann als Qualitätssicherungsmaßnahme Therapeut:innen in ihrer klinischen Arbeit unterstützen und zugleich eine belastbare Datengrundlage für die Psychotherapieforschung schaffen. Im Rahmen der Qualitätssicherung können die erhobenen Daten als zusätzliche Informationsquelle den klinischen Eindruck der Therapeut:innen erweitern und zur Evaluation der Behandlung am Einzelfall aber auch auf der Ebene des Versorgungssystems herangezogen werden. Darüber hinaus können prognostische Vorhersagen von Therapieergebnissen und Abbruchwahrscheinlichkeiten, Behandlungsempfehlungen sowie adaptive Behandlungsanpassungen während der Behandlung auf ihnen aufgebaut werden, die Therapeut:innen in ihren klinischen Entscheidungen unterstützen 1. Eine solche daten-gestützte und evidenzbasierte psychologische Psychotherapie kann die wissenschaftliche Fundierung der therapeutischen Herangehensweise und die Wirksamkeit der Behandlung verbessern. Dazu bedarf es umfangreicher Datenerhebungen, die verlässliche und aussagekräftige Forschungsbefunde ermöglichen 2.

The addition of employment support alongside psychological therapy enhances the chance of recovery for clients most at risk of poor clinical outcomes

BACKGROUND

Many people achieve positive outcomes from psychological therapies for anxiety and depression. However, not everyone benefits and some may require additional support. Previous studies have examined the demographic and clinical characteristics of people starting treatment and identified a patient profile that is associated with poor clinical outcomes.

AIMS

To examine whether the addition of employment-related support alongside psychological therapy was associated with a greater chance of recovery for clients belonging to this patient profile.

METHOD

We analysed 302 clients across three services, who were offered employment-related support alongside psychological therapy. The rate of clinical recovery (falling below clinical thresholds on measures of both anxiety and depression) was compared between individuals who accepted the offer and those who declined, while adjusting for potential confounders.

RESULTS

Logistic regression showed that receiving employment support was significantly associated with clinical recovery after controlling for baseline anxiety and depression scores, the number of psychological treatment sessions, and other clinical and demographic variables. The odds of recovery were 2.54 times greater if clients received employment support; 47% of clients who received employment support alongside psychological therapy were classified as recovered, compared with 27% of those receiving psychological therapy only.

CONCLUSIONS

Providing employment support alongside therapy may be particularly helpful for clients belonging to this patient profile, who represent approximately 10% of referrals to NHS Talking Therapies for Anxiety and Depression services. Services could consider how to increase the provision and uptake of employment-focused support to enhance clients' clinical outcomes.

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.

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.

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.

[Digitization and Machine Learning in Psychotherapy Research and Clinical Practice - Potentials and Problems]

Im Zuge des weltweiten Anstieges der Bedeutung von psychischen Störungen 1, werden frühzeitige Interventionen und wirksame psychotherapeutische Behandlungen für ein funktionierendes Gesundheitssystem immer wichtiger. Der aktuelle Stand der Psychotherapieforschung zeigt jedoch, dass nicht alle Patient:innen gleichermaßen von Psychotherapie profitieren, sondern dass die meisten Patient:innen (70–80%) zwar deutliche Verbesserung zeigen, während andere nur geringe oder keine Fortschritte erzielen oder sogar Verschlechterungen erfahren 2. Dies impliziert eine stärkere Berücksichtigung von individuellen Unterschieden von Patient:innen und deren Therapieverlauf in der Psychotherapieforschung, sowie eine stärkere Refokussierung auf ungünstige Therapieverläufe und Abkehr von der Frage nach durchschnittlichen Unterschieden zwischen den verschiedenen Therapieverfahren oder Therapieschulen.

Predicting Youth and Young Adult Treatment Engagement in a Transdiagnostic Remote Intensive Outpatient Program: Latent Profile Analysis

BACKGROUND

The youth mental health crisis in the United States continues to worsen, and research has shown poor mental health treatment engagement. Despite the need for personalized engagement strategies, there is a lack of research involving youth. Due to complex youth developmental milestones, there is a need to better understand clinical presentation and factors associated with treatment engagement to effectively identify and tailor beneficial treatments.

OBJECTIVE

This quality improvement investigation sought to identify subgroups of clients attending a remote intensive outpatient program (IOP) based on clinical acuity data at intake, to determine the factors associated with engagement outcomes for clients who present in complex developmental periods and with cooccurring conditions. The identification of these subgroups was used to inform programmatic decisions within this remote IOP system.

METHODS

Data were collected as part of ongoing quality improvement initiatives at a remote IOP for youth and young adults. Participants included clients (N=2924) discharged between July 2021 and February 2023. A latent profile analysis was conducted using 5 indicators of clinical acuity at treatment entry, and the resulting profiles were assessed for associations with demographic factors and treatment engagement outcomes.

RESULTS

Among the 2924 participants, 4 profiles of clinical acuity were identified: a low-acuity profile (n=943, 32.25%), characterized by minimal anxiety, depression, and self-harm, and 3 high-acuity profiles defined by moderately severe depression and anxiety but differentiated by rates of self-harm (high acuity+low self-harm: n=1452, 49.66%; high acuity+moderate self-harm: n=203, 6.94%; high acuity+high self-harm: n=326, 11.15%). Age, gender, transgender identity, and sexual orientation were significantly associated with profile membership. Clients identified as sexually and gender-marginalized populations were more likely to be classified into high-acuity profiles than into the low-acuity profile (eg, for clients who identified as transgender, high acuity+low self-harm: odds ratio [OR] 2.07, 95% CI 1.35-3.18; P<.001; high acuity+moderate self-harm: OR 2.85, 95% CI 1.66-4.90; P<.001; high acuity+high self-harm: OR 3.67, 95% CI 2.45-5.51; P<.001). Race was unrelated to the profile membership. Profile membership was significantly associated with treatment engagement: youth and young adults in the low-acuity and high-acuity+low-self-harm profiles attended an average of 4 fewer treatment sessions compared with youth in the high-acuity+moderate-self-harm and high-acuity+high-self-harm profiles (ꭓ23=27.6, P<.001). Individuals in the high-acuity+low-self-harm profile completed treatment at a significantly lower rate relative to the other 2 high-acuity profiles (ꭓ23=13.4, P=.004). Finally, those in the high-acuity+high-self-harm profile were significantly less likely to disengage early relative to youth in all other profiles (ꭓ23=71.12, P<.001).

CONCLUSIONS

This investigation represents a novel application for identifying subgroups of adolescents and young adults based on clinical acuity data at intake to identify patterns in treatment engagement outcomes. Identifying subgroups that differentially engage in treatment is a critical first step toward targeting engagement strategies for complex populations.

Routine Outcome Monitoring (ROM) and Feedback: Research Review and Recommendations

OBJECTIVE

To provide a research review of the components and outcomes of routine outcome monitoring (ROM) and recommendations for research and therapeutic practice.

METHOD

A narrative review of the three phases of ROM - data collection, feeding back data, and adapting therapy - and an overview of patient outcomes from 11 meta-analytic studies.

RESULTS

Patients support ROM when its purpose is clear and integrated within therapy. Greater frequency of data collection is more important for shorter-term therapies, and use of graphs, greater specificity of feedback, and alerts are helpful. Overall effects on patient outcomes are statistically significant (g ≈ 0.15) and increase when clinical support tools (CSTs) are used for not-on-track cases (g ≈ 0.36-0.53). Effects are additive to standard effects of psychological therapies. Organizational, personnel, and resource issues remain the greatest obstacles to the successful adoption of ROM.

CONCLUSION

ROM offers a low-cost method for enhancing patient outcomes, on average resulting in an ≈ 8% advantage (success rate difference; SRD) over standard care. CSTs are particularly effective for not-on-track patients (SRD between ≈ 20% and 29%), but ROM does not work for all patients and successful implementation is a major challenge, along with securing appropriate cultural adaptations.

Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes

OBJECTIVE

The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods.

METHOD

Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F₁-Measure.

RESULTS

Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F₁-Score of .51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended.

CONCLUSION

Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.

Is it me, is it you or is it both of US? Applying the social relations model to disentangle the components of the therapeutic bond

OBJECTIVE

The study investigated the contribution of therapists and patients to the therapeutic bond and their associations (at the within and between levels) to treatment outcome. On this aim, the social relations model (SRM, aimed to analyze dyadic interpersonal data) was implemented.

METHOD

A novel design for individual psychotherapy studies was adopted, a many-with-many asymmetrical block dyadic design, in which several patients interact with several therapists. Hierarchical linear models were computed to study through variance partitioning the different components of the SRM and their association to treatment outcome.

RESULTS

All SRM components (with significant effects at therapist- and patient- within and between levels) resulted in significant contributions to the bond. However, only components at the within- and between-therapist, and within-patient levels resulted in significant associations with outcome.

CONCLUSION

Given the dyadic nature of the bond, our results support not only studying and offering clinical training on interpersonal therapeutic skills but also on constant monitoring and feedback of the relationship at the more idiosyncratic level.

Risiken und Chancen der Digitalisierung

Zusammenfassung. Durch die Digitalisierung ist innerhalb von kurzer Zeit eine zusätzliche Lebenswelt für Kinder und Jugendliche entstanden. Aus kinderpsychologischer Sicht stellt sich deshalb die Fragen, welche Risiken und Chancen die Digitalisierung sowohl für die Kindesentwicklung als auch für die psychotherapeutische Versorgung birgt. Risiken der Digitalisierung, sogenannte online harms („Online-Schäden“), zeigen sich durch Aggravation oder Veränderung bereits bekannter Störungsbilder (z. B. Essstörungen, selbstverletzendes oder suizidales Verhalten, Hypochondrie) oder die Entstehung neuer Störungsbilder (z. B. Online-Verhaltenssüchte wie die Computerspielstörung). Ausschlaggebend erscheint dabei nicht die Zeit, die mit digitalen Medien verbracht wird, sondern das Nutzungsmuster und die Funktionalität der Nutzung. Gleichzeitig bieten digitale Diagnostik, Monitoring- und Feedbacksysteme sowie digitale Interventionen neue Chancen zur Verbesserung der psychotheapeutischen Versorgung. Beispielsweise können Jugendliche durch App-basierte Interventionen im Alltag besser erreicht werden, und auch für unterversorgte Populationen wie beispielsweise Angehörige bieten digitale Angebote einen niederschwelligen Zugang. Die Digitalisierung stellt somit Wissenschaft und Psychotherapie vor große Herausforderungen und neue Aufgaben.

The Trier Treatment Navigator (TTN) in action: Clinical case study on data-informed psychological therapy

BACKGROUND

Although psychotherapy has shown to be effective for most patients, about one-third of patients do not benefit or deteriorate during treatment. Technical progress has allowed the integration of routine outcome monitoring (ROM) into treatment, helping therapists detect patients at risk for a nonresponse or poor outcome early on. Psychological therapy can be enhanced by providing therapists with individual treatment recommendations for these at-risk patients. One example of such a comprehensive feedback system is the Trier Treatment Navigator (TTN).

OBJECTIVE/METHOD

This clinical case study aims to illustrate the implementation of the TTN in a cognitive behavioral therapy (CBT) outpatient clinic in the treatment of a 30-year-old patient called Ms. Daun, who has a recurrent depressive disorder. Based on this case, the benefits of applying information from ROM in psychotherapy, important context factors, and possible implementation issues are discussed.

RESULTS/CONCLUSION

We conclude by encouraging practitioners to integrate ROM into their clinical thinking and daily practice.

Movement-based patient-therapist attunement in psychological therapy and its association with early change

Objective

Attunement is a novel measure of nonverbal synchrony reflecting the duration of the present moment shared by two interaction partners. This study examined its association with early change in outpatient psychotherapy.

Methods

Automated video analysis based on motion energy analysis (MEA) and cross-correlation of the movement time-series of patient and therapist was conducted to calculate movement synchrony for N = 161 outpatients. Movement-based attunement was defined as the range of connected time lags with significant synchrony. Latent change classes in the HSCL-11 were identified with growth mixture modeling (GMM) and predicted by pre-treatment covariates and attunement using multilevel multinomial regression.

Results

GMM identified four latent classes: high impairment, no change (Class 1); high impairment, early response (Class 2); moderate impairment (Class 3); and low impairment (Class 4). Class 2 showed the strongest attunement, the largest early response, and the best outcome. Stronger attunement was associated with a higher likelihood of membership in Class 2 (b = 0.313, p = .007), Class 3 (b = 0.251, p = .033), and Class 4 (b = 0.275, p = .043) compared to Class 1. For highly impaired patients, the probability of no early change (Class 1) decreased and the probability of early response (Class 2) increased as a function of attunement.

Conclusions

Among patients with high impairment, stronger patient-therapist attunement was associated with early response, which predicted a better treatment outcome. Video-based assessment of attunement might provide new information for therapists not available from self-report questionnaires and support therapists in their clinical decision-making.