The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration

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.

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project

BACKGROUND

Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project.

METHODS

In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis.

RESULTS

Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities.

CONCLUSIONS

This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Psychotherapy process research: Identifying productive in-session processes to enhance treatment outcomes and therapist responsiveness

This paper provides an overview of my research programme for the past 37 years. The focus of my work has been on identifying productive in-session processes to enhance treatment outcomes and therapist responsiveness. Two foci will be reviewed, first, my research on client and therapist interpersonal process and second, productive processing in psychotherapy in three different therapeutic approaches including EFT, CBT and CCT. Given that many competing theoretical perspectives are effective, I was curious about change processes that are common and unique to each. In my work, I employed a variety of research methodologies drawing on frameworks with alternative epistemological and ontological assumptions to capture specific in-session change processes in an attempt to reveal the richness and complexity of the phenomena being studied and illuminate the process of change.

Effectiveness of attachment-based family therapy for suicidal adolescents and young adults: A systematic review and meta-analysis

Background

Suicide is a leading cause of death among adolescents and young adults. While only few evidence-based treatments with limited efficacy are available, family processes have recently been posed as a possible alternative target for intervention. Here, we review the evidence for Attachment-Based Family Therapy (ABFT), a guideline-listed treatment targeting intrafamilial ruptures and building protective caregiver-child relationships.

Method

PubMed, PsycINFO, Embase, and Scopus were searched for prospective trials on ABFT in youth published up until November 6th, 2023, and including measures of suicidality. Results were independently screened by two researchers following PRISMA guidelines. Risk of bias was assessed using the Cochrane RoB-2 framework. A random effects meta-analysis was conducted on suicidal ideation and depressive symptoms post-intervention scores in randomized-controlled trials (RCTs).

Results

Seven articles reporting on four RCTs (n = 287) and three open trials (n = 45) were identified. Mean age of participants was M pooled = 15.2 years and the majority identified as female (~80%). Overall, ABFT was not significantly more effective in reducing youth suicidal ideation, gpooled = 0.40, 95% CI [-0.12, 0.93], nor depressive symptoms, gpooled = 0.33, 95% CI [-0.18, 0.84], compared to investigated controls (Waitlist, (Enhanced) Treatment as Usual, Family-Enhanced Nondirective Supportive Therapy).

Conclusion

Evidence is strongly limited, with few available trials, small sample sizes, high sample heterogeneity, attrition rates, and risk of bias. While not generally superior to other treatments, ABFT might still be a clinically valid option in specific cases and should be further investigated. Clinicians are currently recommended to apply caution when considering ABFT as stand-alone intervention for suicidal youth and to decide on a case-by-case basis.

Advancing the personalized advantage index (PAI): a systematic review and application in two large multi-site samples in anxiety disorders

BACKGROUND

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

METHODS

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

RESULTS

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

CONCLUSION

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

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 longitudinal view of an approach to responsiveness: Principles followed and lessons learned

AbstractResponsiveness is currently a hot topic in the psychotherapy literature with a large variation in what the term means to colleagues of various orientations. This adds to its popularity but limits the scope of whatever is written or said about responsiveness. The fact that the meaning of responsiveness has developed over time within the approaches adds also to the variation, while an understanding of development has the potential of deepening the understanding of each approach. As a fair description and comparison of even just the most important approaches is by far out of reach for a page-limited article, the development of one approach, which may be termed the "Bernese" approach is described here, along with lessons learnt and general comments. The approach includes Plan Analysis case formulations, the concept of complementary or Motive-Oriented Relationship, a description of a combined qualitative and quantitative assessment, and many methodological and conceptual considerations. Personal development is woven in. Overall, it seems fair to say that this approach, at its core developed long before responsiveness became popular, has turned out to still be useful, with a gain in depth as far as concepts and assessment are concerned.

Predicting optimal treatment allocation for cognitive analytic-guided self-help versus cognitive behavioural-guided self-help

OBJECTIVES

Given the ubiquity in routine services of low-intensity guided self-help (GSH) psychological interventions, better patient selection for these brief interventions would be organizationally efficient. This study therefore sought to define who would respond best to two different types of GSH for anxiety to enable better future treatment matching.

METHODS

The study used outcome data from a patient preference trial (N = 209) comparing cognitive analytic therapy-guided self-help (CAT-GSH) with cognitive behavioural therapy-guided self-help (CBT-GSH). Elastic Net regularization and Boruta random forest variable selection methods were applied. Regression models calculated the patient advantage index (PAI) to designate which GSH was likely the most effective for each patient. Outcomes were compared for those receiving their PAI-indicated optimal and non-optimal GSH.

RESULTS

Lower baseline depression and anxiety severity predicted better outcomes for both types of GSH. Patient preference status was not associated with outcome during either GSH. Sixty-three % received their model indicating optimal GSH and these had significantly higher rates of reliable and clinically significant reductions in anxiety at both post-treatment (35.9% vs. 16.6%) and follow-up (36.6% vs. 19.2%). No single patient with a large PAI had a reliable and clinically significant reduction in anxiety at post-treatment or follow-up when they did not receive their optimal GSH.

CONCLUSIONS

Treatment matching algorithms have the potential to support evidenced-based treatment selection for GSH. Treatment selection and supporting patient choice needs to be integrated. Future research needs to investigate the use of the PAI for GSH treatment matching, but with larger and more balanced samples.

Predictors of treatment outcome in cognitive behavioural therapy for chronic pain: a systematic review

PURPOSE

The aim of this systematic review was to synthesise the research identifying possible influences on CBT outcomes in chronic pain. Variations in the effectiveness of psychological therapies, such as CBT, in chronic pain have led to research investigating predictors of improved treatment outcomes.

MATERIALS AND METHODS

We identified randomised controlled and cohort studies of CBT for chronic pain, published between 1974 to 2nd August 2023, which identified predictors of CBT outcomes.

RESULTS

Nineteen studies were included in the review. Baseline sociodemographic, physical and emotional factors that influence the outcomes of CBT for chronic pain were identified. The most commonly reported predictors of CBT outcome, with medium to large effect sizes, were anxiety, depression and negative cognitions about pain and coping. Sociodemographic predictors of outcomes demonstrated small effects and lacked replicability.

CONCLUSIONS

There was variability across study designs, CBT delivery and outcomes measures. Further research is needed in chronic pain to identify the predictive factors which influence treatment outcomes, and consistency across study designs and outcome variables is needed to reduce heterogeneity.

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.

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners

Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual patient. In this tutorial, we describe different meta-learners, which are flexible algorithms that can be used to estimate personalized treatment effects. More specifically, meta-learners decompose treatment effect estimation into multiple prediction tasks, each of which can be solved by any machine learning model. We begin by reviewing necessary assumptions for interpreting the estimated treatment effects as causal, and then give an overview over key concepts of machine learning. Throughout the article, we use an illustrative data example to show how the different meta-learners can be implemented in R. We also point out how current popular practices in psychotherapy research fit into the meta-learning framework. Finally, we show how heterogeneous treatment effects can be analyzed, and point out some challenges in the implementation of meta-learners.

Facilitative interpersonal skills in benign versus challenging therapy situations in trainee therapists: a pilot study

Therapists' responses to challenging therapy situations on the Facilitative Interpersonal Skills (FIS) performance task are a significant predictor of therapists' differences in treatment outcomes. The aim of this study was to assess whether the complexity of the therapy situation influenced the facilitative interpersonal skills of trainees. Trainee therapists (n=46) participated in an experiment in which they responded to a set of challenging and benign (i.e., non-challenging) video vignettes of therapy situations of the FIS performance task. Their responses were video recorded and coded by four independent raters. Results showed that trainees scored significantly higher on the FIS performance task responding to benign therapy situations than responding to challenging situations. This is the first study to investigate difficulty of therapy situations as a potential predictor or trainees interpersonal skills. Further research is needed to replicate these results in a larger sample.

Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study

BACKGROUND

Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems.

METHODS

This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203).

RESULTS

Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU.

LIMITATIONS

Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables.

CONCLUSIONS

Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

Prevalence Increases as Treatments Improve: An Evolutionary Perspective on the Treatment-Prevalence Paradox in Depression

Depression is an eminently treatable disorder that responds to psychotherapy or medications; the efficacy of each has been established in hundreds of controlled trials. Nonetheless, the prevalence of depression has increased in recent years despite the existence of efficacious treatments-a phenomenon known as the treatment-prevalence paradox. We consider several possible explanations for this paradox, which range from a misunderstanding of the very nature of depression, inflated efficacy of the established treatments, and a lack of access to efficacious delivery of treatments. We find support for each of these possible explanations but especially the notion that large segments of the population lack access to efficacious treatments that are implemented as intended. We conclude by describing the potential of using lay therapists and digital technologies to overcome this lack of access and to reach historically underserved populations and simultaneously guarantee the quality of the interventions delivered.

What CBT Modules Work Best for Whom? Identifying Subgroups of Depressed Youths by Their Differential Response to Specific Modules

Prior research suggests that the effects of specific cognitive-behavioral therapy (CBT) modules on symptom outcomes can be estimated. We conducted a study utilizing idiographic and nomothetic methods to clarify which CBT modules are most effective for youth depression, and for whom they are most effective. Thirty-five youths received modular CBT for depression. Interrupted time series models estimated whether the introduction of each module was associated with changes in internalizing symptoms, whereby significant symptom reduction would suggest a therapeutic response to the module. Regression models were used to explore whether participant characteristics predicted subgroups of youths based on their estimated response to certain types (e.g., cognitive) of modules, and whether group membership was associated with posttreatment outcomes. Thirty youths (86%) had at least one module associated with a significant change in internalizing symptoms from premodule delivery to postmodule delivery. The specific modules associated with these changes varied across youths. Behavioral activation was most frequently associated with symptom decreases (34% of youths). No participant characteristics predicted estimated response to module type, and group membership was not significantly associated with posttreatment outcomes. Youths display highly heterogeneous responses to treatment modules, indicating multiple pathways to symptom improvement for depressed youths.

Individual treatment selection for patients with post-traumatic stress disorder: External validation of a personalised advantage index

OBJECTIVE

To test the predictive accuracy and generalisability of a personalised advantage index (PAI) model designed to support treatment selection for Post-Traumatic Stress Disorder (PTSD).

METHOD

A PAI model developed by Deisenhofer et al. (2018) was used to predict treatment outcomes in a statistically independent dataset including archival records for N = 152 patients with PTSD who accessed either trauma-focussed cognitive behavioural therapy or eye movement desensitisation and reprocessing in routine care. Outcomes were compared between patients who received their PAI-indicated optimal treatment versus those who received their suboptimal treatment.

RESULTS

The model did not yield treatment specific predictions and patients who had received their PAI-indicated optimal treatment did not have better treatment outcomes in this external validation sample.

CONCLUSION

This PAI model did not generalise to an external validation sample.

Understanding symptom profiles of depression with the PHQ-9 in a community sample using network analysis

BACKGROUND

Depression is one of the most prevalent mental health conditions in the world. However, the heterogeneity of depression has presented obstacles for research concerning disease mechanisms, treatment indication, and personalization. The current study used network analysis to analyze and compare profiles of depressive symptoms present in community samples, considering the relationship between symptoms.

METHODS

Cross-sectional measures of depression using the Patient Health Questionnaire - 9 items (PHQ-9) were collected from community samples using data from participants scoring above a clinical threshold of ≥10 points (N = 2,023; 73.9% female; mean age 49.87, SD = 17.40). Data analysis followed three steps. First, a profiling algorithm was implemented to identify all possible symptom profiles by dichotomizing each PHQ-9 item. Second, the most prevalent symptom profiles were identified in the sample. Third, network analysis for the most prevalent symptom profiles was carried out to identify the centrality and covariance of symptoms.

RESULTS

Of 382 theoretically possible depression profiles, only 167 were present in the sample. Furthermore, 55.6% of the symptom profiles present in the sample were represented by only eight profiles. Network analysis showed that the network and symptoms' relationship varied across the profiles.

CONCLUSIONS

Findings indicate that the vast number of theoretical possible ways to meet the criteria for major depressive disorder (MDD) is significantly reduced in empirical samples and that the most common profiles of symptoms have different networks and connectivity patterns. Scientific and clinical consequences of these findings are discussed in the context of the limitations of this study.

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.

Clinical Judgments of Response Profiles: Do They Tell Us What Matters for Whom?

DeRubeis and colleagues (2014a) proposed that psychotherapy research has been limited by underappreciated variability in how patients respond to psychotherapy. They proposed that the relationship between the quality of therapy and outcome varies according to patient response profiles. In a study of cognitive-behavioral therapy (CBT) for depression, we tested clinician ratings of this construct as a moderator of the relationship between therapist adherence to cognitive or behavioral methods in predicting symptom change. Patients (N = 125) participated in CBT for depression. Assessors rated response profiles following the intake and therapists rated them after the first session. We collected data on adherence at the first five sessions and symptoms at the first six sessions. Therapist ratings following the first session, but not assessor ratings at intake, moderated the relationship between each form of adherence and symptom change. Patients given lower ratings (identifying them as spontaneous remitting or easy patients) had a stronger relationship between adherence and greater symptom change, with this relationship reversed such that adherence was related to less robust symptom change for those with the highest ratings (intractable or challenging patients). Our findings suggest promise for clinical evaluation of response profiles. We encourage future research evaluating refinements to such measures.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Effectiveness of cognitive behavioral group therapy, psychodrama, and their integration for treatment of social anxiety disorder: A randomized controlled trial

BACKGROUND AND OBJECTIVES

Although cognitive behavioral group therapy (CBGT) is an effective treatment for social anxiety disorder, many socially anxious patients are still symptomatic after treatment. A possible improvement for CBGT could come from the more experiential group psychotherapy, psychodrama (PD). The integration of CBGT and PD (labeled CBPT) might offer an even more effective treatment than CBGT or PD alone. With the present study, we investigated first whether three kinds of group therapy (CBGT, PD, and CBPT) are superior to a waitlist (WL). Second, we investigated whether CBPT is more effective than CBGT or PD alone.

METHODS

One hundred and forty-four social anxiety patients were randomly assigned to three active conditions or a WL. After wait, WL-participants were randomized over the active treatment conditions.

RESULTS

The results of a multilevel analysis showed that all treatments were superior to WL in reducing social anxiety complaints. Only CBGT and CBPT differed significantly from WL in reducing fear of negative evaluations. There were no significant differences between active conditions in any of the variables after treatment and after six-month follow up, neither were there significant differences in treatment dropout.

LIMITATIONS

First there is the lack of a long-term follow-up. Second, because of loss of participants, we did not reach the planned numbers in the active treatment groups in comparison to WL. Moreover, this study was not designed as a non-inferiority or equivalence trial.

CONCLUSIONS

Although the integrative CBPT showed good results, it was not more effective than the other treatments.

Identifying who benefits most from supportive versus expressive techniques in psychotherapy for depression: Moderators of within- versus between-individual effects

OBJECTIVE

A recent randomized controlled trial (RCT) indicated that individuals with higher levels of attachment anxiety exhibited better treatment outcomes in supportive-expressive therapy (SET) relative to supportive therapy (ST). But to gain insight into within-patient therapeutic changes, a within-individual design is required. The present study contrasts previous findings based on theory-driven between-patient moderators with data-driven moderators of within-patient processes to investigate whether findings converge or diverge across these two approaches.

METHOD

We used data of 118 patients from the pilot and active phases of a recent RCT for patients with major depressive disorder, comparing ST with SET, a time-limited psychodynamic therapy. The predefined primary outcome measure was the Hamilton Rating Scale for Depression. Supportive versus expressive techniques were rated based on patients' end-of-session perspective. We compared previous findings based on moderators of between-patient effects with a data-driven approach for identifying moderators of within-patient effects of techniques on subsequent outcome.

RESULTS

After false discovery rate corrections, of 10 preselected moderators, patients' attachment anxiety and domineering style remained significant. Of these, bootstrap resampling revealed significant differences between ST and SET techniques for the attachment anxiety moderator: Those with higher attachment anxiety benefited more from greater use of ST than SET techniques in a particular session, as evidenced by lower levels of symptoms at the subsequent session.

CONCLUSIONS

Our within-individual findings diverge from previously published between-individual analyses. This proof-of-concept study demonstrates the importance of complementing between-individuals with within-individual analyses to achieve better understanding of who benefits most from specific treatment techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment

In this narrative review, we survey recent empirical evaluations of AI-based language assessments and present a case for the technology of large language models to be poised for changing standardized psychological assessment. Artificial intelligence has been undergoing a purported "paradigm shift" initiated by new machine learning models, large language models (e.g., BERT, LAMMA, and that behind ChatGPT). These models have led to unprecedented accuracy over most computerized language processing tasks, from web searches to automatic machine translation and question answering, while their dialogue-based forms, like ChatGPT have captured the interest of over a million users. The success of the large language model is mostly attributed to its capability to numerically represent words in their context, long a weakness of previous attempts to automate psychological assessment from language. While potential applications for automated therapy are beginning to be studied on the heels of chatGPT's success, here we present evidence that suggests, with thorough validation of targeted deployment scenarios, that AI's newest technology can move mental health assessment away from rating scales and to instead use how people naturally communicate, in language.

Development of decision rules for an adaptive aftercare intervention based on individual symptom courses for agoraphobia patients

As other mental illnesses, agoraphobia is associated with a significant risk for relapse after the end of treatment. Personalized and adaptive approaches appear promising to improve maintenance treatment and aftercare as they acknowledge patients' varying individual needs with respect to intensity of care over time. Currently, there is a deficit of knowledge about the detailed symptom course after discharge from acute treatment, which is a prerequisite for the empirical development of rules to decide if and when aftercare should be intensified. Therefore, this study aimed firstly at the investigation of the naturalistic symptom course of agoraphobia after discharge from initial treatment and secondly at the development and evaluation of a data-driven algorithm for a digital adaptive aftercare intervention. A total of 56 agoraphobia patients were recruited in 3 hospitals. Following discharge, participants completed a weekly online monitoring assessment for three months. While symptom severity remained stable at the group level, individual courses were highly heterogeneous. Approximately two-thirds of the patients (70%) reported considerable symptoms at some time, indicating a need for medium or high-intense therapeutic support. Simulating the application of the algorithm to the data set resulted in an early (86% before week six) and relatively even allocation of patients to three groups (need for no, medium, and high-intense support respectively). Overall, findings confirm the need for adaptive aftercare strategies in agoraphobia. Digital, adaptive approaches may provide immediate support to patients who experience symptom deterioration and thus promise to contribute to an optimized allocation of therapeutic resources and overall improvement of care.

Moderators of short- and long-term outcomes in panic control treatment and panic-focused psychodynamic psychotherapy

OBJECTIVE

The objective was to test the hypothesis that externalizing and internalizing helpfulness beliefs and learning styles at baseline moderate panic severity and overall mental illness as short-term and long-term outcomes of two panic-focused psychotherapies, Panic Control Treatment (PCT) and Panic-Focused Psychodynamic Psychotherapy (PFPP).

METHOD

Participants were 108 adults with DSM-IV Panic Disorder with or without Agoraphobia (PD/A) who were randomized to treatment in a trial of PCT and PFPP. Piece-wise/segmented multilevel modeling was used to test three-way interactions (Treatments × Moderator × Time), with participants and therapists as random factors. Outcome variables were clinician-rated panic severity and self-rated mental illness post-treatment and during follow-up.

RESULTS

Patients' externalizing (but not internalizing) helpfulness beliefs moderated mental illness outcomes during follow-up (but not during treatment); low levels of Externalization were facilitative for PFPP but not PCT. Internalizing and externalizing helpfulness beliefs and learning style did not moderate clinician-rated panic severity, whether short- or long-term.

CONCLUSIONS

These results suggest that helpfulness beliefs and learning style have limited use in assignment to either PCT or PFPP for PD/A. Although further research is needed, low levels of helpfulness beliefs about externalizing coping may play a role in mental illness outcomes for PFPP.

Illusory generalizability of clinical prediction models

It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.

Telepsychiatry in the Treatment of Major Depressive Disorders

This chapter explores the transformative role of telepsychiatry in managing major depressive disorders (MDD). Traversing geographical barriers and reducing stigma, this innovative branch of telemedicine leverages digital platforms to deliver effective psychiatric care. We investigate the evolution of telepsychiatry, examining its diverse interventions such as videoconferencing-based psychotherapy, medication management, and mobile applications. While offering significant advantages like increased accessibility, cost-effectiveness, and improved patient engagement, challenges in telepsychiatry include technological barriers, privacy concerns, ethical and legal considerations, and digital literacy gaps. Looking forward, emerging technologies like virtual reality, artificial intelligence, and precision medicine hold immense potential to personalize and enhance treatment effectiveness. Recognizing its limitations and advocating for equitable access, this chapter underscores telepsychiatry's power to revolutionize MDD treatment, making quality mental healthcare a reality for all.

Revealing subgroup-specific mechanisms of change via moderated mediation: A meditation intervention example

OBJECTIVE

Effective psychosocial interventions exist for numerous mental health conditions. However, despite decades of research, limited progress has been made in clarifying the mechanisms that account for their beneficial effects. We know that many treatments work, but we know relatively little about why they work. Mechanisms of change may be obscured due to prior research collapsing across heterogeneous subgroups of patients with differing underlying mechanisms of response. Studies identifying baseline individual characteristics that predict differential response (i.e., moderation) may inform research on why (i.e., mediation) a particular subgroup has better outcomes to an intervention via tests of moderated mediation.

METHOD

In a recent randomized controlled trial comparing a 4-week meditation app with a control condition in school system employees (N = 662), we previously developed a "Personalized Advantage Index" (PAI) using baseline characteristics, which identified a subgroup of individuals who derived relatively greater benefit from meditation training. Here, we tested whether the effect of mindfulness acquisition in mediating group differences in outcome was moderated by PAI scores.

RESULTS

A significant index of moderated mediation (IMM = 1.22, 95% CI [0.30, 2.33]) revealed that the effect of mindfulness acquisition in mediating group differences in outcome was only significant among those individuals with PAI scores predicting relatively greater benefit from the meditation app.

CONCLUSIONS

Subgroups of individuals may differ meaningfully in the mechanisms that mediate their response to an intervention. Considering subgroup-specific mediators may accelerate progress on clarifying mechanisms of change underlying psychosocial interventions and may help inform which specific interventions are most beneficial for whom. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research Reviews: Advances in methods for evaluating child and adolescent mental health interventions

BACKROUND

The evidence base for interventions for child mental health and neurodevelopment is weak and the current capacity for rigorous evaluation limited. We describe some of the challenges that make this field particularly difficult and expensive for evaluation studies.

METHODS

We describe and review the use of novel study designs and analysis methodology for their potential to improve this situation.

RESULTS

While several novel designs appeared ill-suited to our field, systematic review found others that offered potential but had yet to be widely adopted, some not at all.

CONCLUSIONS

While funding is inevitably a constraint, we argue that improvements in the evidence base of both current and new treatments will only be achieved by the adoption of a number of these new technologies and study designs, the consistent application of rigorous constructive but demanding standards, and the engagement of the public, patients, clinical and research services to build a design, recruitment, and analysis infrastructure.

Evaluating a treatment selection approach for online single-session interventions for adolescent depression

BACKGROUND

The question 'what works for whom' is essential to mental health research, as matching individuals to the treatment best suited to their needs has the potential to maximize the effectiveness of existing approaches. Digitally administered single-session interventions (SSIs) are effective means of reducing depressive symptoms in adolescence, with potential for rapid, large-scale implementation. However, little is known about which SSIs work best for different adolescents.

OBJECTIVE

We created and tested a treatment selection algorithm for use with two SSIs targeting depression in high-symptom adolescents from across the United States.

METHODS

Using data from a large-scale RCT comparing two evidence-based SSIs (N = 996; ClinicalTrials.gov: NCT04634903), we utilized a Personalized Advantage Index approach to create and evaluate a treatment-matching algorithm for these interventions. The two interventions were Project Personality (PP; N = 482), an intervention teaching that traits and symptoms are malleable (a 'growth mindset'), and the Action Brings Change Project (ABC; N = 514), a behavioral activation intervention.

RESULTS

Results indicated no significant difference in 3-month depression outcomes between participants assigned to their matched intervention and those assigned to their nonmatched intervention. The relationship between predicted response to intervention (RTI) and observed RTI was weak for both interventions (r = .39 for PP, r = .24 for ABC). Moreover, the correlation between a participants' predicted RTI for PP and their predicted RTI for ABC was very high (r = .79).

CONCLUSIONS

The utility of treatment selection approaches for SSIs targeting adolescent depression appears limited. Results suggest that both (a) predicting RTI for SSIs is relatively challenging, and (b) the factors that predict RTI for SSIs are similar regardless of the content of the intervention. Given their overall effectiveness and their low-intensity, low-cost nature, increasing youths' access to both digital SSIs may carry more public health utility than additional treatment-matching efforts.

Predicting optimal treatment outcomes in phase-based treatment and direct trauma-focused treatment among patients with posttraumatic stress disorder stemming from childhood abuse

Research over the last few decades has demonstrated the effectiveness of various treatments for posttraumatic stress disorder (PTSD). However, the question of which treatment works best remains, especially for patients with PTSD stemming from childhood abuse. Using the Personalized Advantage Index (PAI), we explored which patients benefit more from phase-based treatment and which benefit more from direct trauma-focused treatment. Data were obtained from a multicenter randomized controlled trial (RCT) comparing a phase-based treatment condition (i.e., eye-movement desensitization and reprocessing [EMDR] therapy preceded by Skills Training in Affect and Interpersonal Regulation [STAIR]; n = 57) and a direct trauma-focused treatment (EMDR only; n = 64) among individuals with PTSD related to childhood abuse. Machine learning techniques were used to examine all pretreatment variables included in the trial as potential predictors and moderators, with selected variables combined to build the PAI model. The utility of the PAI was tested by comparing actual posttreatment outcomes of individuals who received PAI-indicated treatment with those allocated to a non-PAI-indicated treatment. Although eight pretreatment variables between PTSD treatment outcome and treatment condition were selected as moderators, there was no significant difference between participants assigned to their PAI-indicated treatment and those randomized to a non-PAI-indicated treatment, d = 0.25, p = .213. Hence, the results of this study do not support the need for personalized medicine for patients with PTSD and a history of childhood abuse. Further research with larger sample sizes and external validation is warranted.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index

Introduction

Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment.

Methods

Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments.

Results

Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures.

Conclusions

Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

Using the Personalized Advantage Index to determine which veterans may benefit from more vs. less comprehensive intensive PTSD treatment programs

Background: Intensive PTSD treatment programs (ITPs) are highly effective but tend to differ greatly in length and the number of adjunctive services that are provided in conjunction with evidence-based PTSD treatments. Individuals' treatment response to more or less comprehensive ITPs is poorly understood.Objective: To apply a machine learning-based decision-making model (the Personalized Advantage Index (PAI)), using clinical and demographic factors to predict response to more or less comprehensive ITPs.Methods: The PAI was developed and tested on a sample of 747 veterans with PTSD who completed a 3-week (more comprehensive; n = 360) or 2-week (less comprehensive; n = 387) ITP.Results: Approximately 12.32% of the sample had a PAI value that suggests that individuals would have experienced greater PTSD symptom change (5 points) on the PTSD Checklist for DSM-5 in either a more- or less comprehensive ITP. For individuals with the highest 25% of PAI values, effect sizes for the amount of PTSD symptom change between those in their optimal vs. non-optimal programs was d = 0.35.Conclusions: Although a minority was predicted to have benefited more from a program, there generally was not a substantial difference in predicted outcomes. Less comprehensive and thus more financially sustainable ITPs appear to work well for most individuals with PTSD.

[Personalized psychotherapy of posttraumatic stress disorder : Overview on the selection of treatment methods and techniques using statistical procedures]

BACKGROUND

A relevant heterogeneity of treatment effects in posttraumatic stress disorder (PTSD) is discussed with respect to the debate about the necessity of phase-based treatment and in light of the new diagnosis of complex PTSD and has recently been proven; however, there has been little personalization in the treatment of PTSD. This article presents the current state of research on the personalized selection of specific psychotherapeutic methods for the treatment of PTSD based on patient characteristics using statistical methods.

METHODS

A systematic literature search was conducted in the PubMed (including Medline), Embase, Web of Science Core Collection, Google Scholar, PsycINFO and PSYNDEX databases to identify clinical trials and reviews examining personalized treatment for PTSD.

RESULTS

A total of 13 relevant publications were identified, of which 5 articles were predictor analyses in samples without control conditions and 7 articles showed analyses of randomized controlled trials (RCT) with a post hoc comparison of treatment effects in optimally and nonoptimally assigned patients. In addition, one article was a systematic review on the treatment of patients with comorbid borderline personality order and PTSD.

DISCUSSION

The available manuscripts indicate the importance and benefits of personalized treatment in PTSD. The relevant predictor variables identified for personalization should be used as a suggestion to investigate them in future prospective studies.

Predictors and moderators of three online interventions for eating disorder symptoms in a randomized controlled trial

OBJECTIVE

To optimize treatment recommendations for eating disorders, it is important to investigate whether some individuals may benefit more (or less) from certain treatments. The current study explored predictors and moderators of an automated online self-help intervention "Featback" and online support from a recovered expert patient.

METHODS

Data were used from a randomized controlled trial. For a period of 8 weeks, participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, (2) chat or e-mail support from an expert patient, (3) Featback with expert-patient support, and (4) a waitlist. A mixed-effects partitioning method was used to see if age, educational level, BMI, motivation to change, treatment history, duration of eating disorder, number of binge eating episodes in the past month, eating disorder pathology, self-efficacy, anxiety and depression, social support, or self-esteem predicted or moderated intervention outcomes in terms of eating disorder symptoms (primary outcome), and symptoms of anxiety and depression (secondary outcome).

RESULTS

Higher baseline social support predicted less eating disorder symptoms 8 weeks later, regardless of condition. No variables emerged as moderator for eating disorder symptoms. Participants in the three active conditions who had not received previous eating disorder treatment, experienced larger reductions in anxiety and depression symptoms.

DISCUSSION

The investigated online low-threshold interventions were especially beneficial for treatment-naïve individuals, but only in terms of secondary outcomes, making them well-suited for early intervention. The study results also highlight the importance of a supportive environment for individuals with eating disorder symptoms.

PUBLIC SIGNIFICANCE

To optimize treatment recommendations it is important to investigate what works for whom. For an internet-based intervention for eating disorders developed in the Netherlands, individuals who had never received eating disorder treatment seemed to benefit more from the intervention than those who had received eating disorder treatment, because they experienced larger reductions in symptoms of depression and anxiety. Stronger feelings of social support were related to less eating disorder symptoms in the future.

[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.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Early Maladaptive Schemas and Schema Modes in clinical disorders: A systematic review

PURPOSE

Although schema therapy has been predominantly applied to treat personality disorders, interest into its application in other clinical disorders is growing. Central to schema therapy are Early Maladaptive Schemas (EMS) and Schema Modes. Since existing EMS and Schema Modes were primarily developed in the context of personality disorders, their relevance for clinical disorders is unclear.

METHODS

We conducted a systematic review of the presence of EMS and Schema Modes in clinical disorders according to DSM criteria. Per disorder, we evaluated which EMS and Schema Modes were more pronounced in comparison with clinical as well as non-clinical control groups and which EMS and Schema Modes were most highly endorsed within the disorder.

RESULTS

Although evidence concerning EMS was scarce for several disorders, and only few studies on Schema Modes survived inclusion criteria, we identified meaningful relationships and patterns for EMS and Schema Modes in various clinical disorders.

CONCLUSIONS

The present review highlights the relevance of EMS and Schema Modes for clinical disorders beyond personality disorders. Depending on the theme of the representation, EMS act as vulnerabilities both across diagnoses and for specific disorders. Thus, EMS and resulting Schema Modes are potential, valuable targets for the prevention and treatment of clinical disorders.

Outcomes of People of Color in an Efficacy Trial of Cognitive-Behavioral Treatments for Anxiety, Depression, and Related Disorders: Preliminary Evidence

ABSTRACT

Although evidence-based psychological treatments such as cognitive behavioral therapy (CBT) have strong empirical support for reducing anxiety and depression symptoms, CBT outcome research often does not report race and ethnicity variables, or assess how well CBT works for people from historically excluded racial and ethnic groups. This study presents post hoc analyses comparing treatment retention and symptom outcomes for participants of color ( n = 43) and White participants ( n = 136) from a randomized controlled efficacy trial of CBT. χ 2 tests and one-way ANCOVA showed no observable differences between the two samples on attrition or on clinician-rated measures of anxiety and depression at posttreatment and follow-up. Moderate to large within-group effect sizes on anxiety and depression were found for Black, Latinx, and Asian American participants at almost all time points. These preliminary findings suggest that CBT for anxiety and comorbid depression may be efficacious for Black, Asian American, and Latinx individuals.

Improving programme-led and focused interventions for eating disorders: An experts' consensus statement-A UK perspective

OBJECTIVE

Eating disorders are associated with significant illness burden and costs, yet access to evidence-based care is limited. Greater use of programme-led and focused interventions that are less resource-intensive might be part of the solution to this demand-capacity mismatch.

METHOD

In October 2022, a group of predominantly UK-based clinical and academic researchers, charity representatives and people with lived experience convened to consider ways to improve access to, and efficacy of, programme-led and focused interventions for eating disorders in an attempt to bridge the demand-capacity gap.

RESULTS

Several key recommendations were made across areas of research, policy, and practice. Of particular importance is the view that programme-led and focused interventions are suitable for a range of different eating disorder presentations across all ages, providing medical and psychiatric risk are closely monitored. The terminology used for these interventions should be carefully considered, so as not to imply that the treatment is suboptimal.

CONCLUSIONS

Programme-led and focused interventions are a viable option to close the demand-capacity gap for eating disorder treatment and are particularly needed for children and young people. Work is urgently needed across sectors to evaluate and implement such interventions as a clinical and research priority.

Efficacy of an Unguided, Digital Single-Session Intervention for Internalizing Symptoms in Web-Based Workers: Randomized Controlled Trial

BACKGROUND

The Common Elements Toolbox (COMET) is an unguided digital single-session intervention (SSI) based on principles of cognitive behavioral therapy and positive psychology. Although unguided digital SSIs have shown promise in the treatment of youth psychopathology, the data are more mixed regarding their efficacy in adults.

OBJECTIVE

This study aimed to investigate the efficacy of COMET-SSI versus a waiting list control in depression and other transdiagnostic mental health outcomes for Prolific participants with a history of psychopathology.

METHODS

We conducted an investigator-blinded, preregistered randomized controlled trial comparing COMET-SSI (n=409) with an 8-week waiting list control (n=419). Participants were recruited from the web-based workspace Prolific and assessed for depression, anxiety, work and social functioning, psychological well-being, and emotion regulation at baseline and at 2, 4, and 8 weeks after the intervention. The main outcomes were short-term (2 weeks) and long-term (8 weeks) changes in depression and anxiety. The secondary outcomes were the 8-week changes in work and social functioning, well-being, and emotion regulation. Analyses were conducted according to the intent-to-treat principle with imputation, without imputation, and using a per-protocol sample. In addition, we conducted sensitivity analyses to identify inattentive responders.

RESULTS

The sample comprised 61.9% (513/828) of women, with a mean age of 35.75 (SD 11.93) years. Most participants (732/828, 88.3%) met the criteria for screening for depression or anxiety using at least one validated screening scale. A review of the text data suggested that adherence to the COMET-SSI was near perfect, there were very few inattentive respondents, and satisfaction with the intervention was high. However, despite being powered to detect small effects, there were negligible differences between the conditions in the various outcomes at the various time points, even when focusing on subsets of individuals with more severe symptoms.

CONCLUSIONS

Our results do not support the use of the COMET-SSI in adult Prolific participants. Future work should explore alternate ways of intervening with paid web-based participants, including matching individuals to SSIs they may be most responsive to.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05379881, https://clinicaltrials.gov/ct2/show/NCT05379881.

Why is it so hard to identify (consistent) predictors of treatment outcome in psychotherapy? - clinical and research perspectives

BACKGROUND

Anxiety and depression are two of the most debilitating psychological disorders worldwide today. Fortunately, effective treatments exist. However, a large proportion of patients do not recover from treatment, and many still have symptoms after completing treatment. Numerous studies have tried to identify predictors of treatment outcome. So far, researchers have found few or no consistent predictors applicable to allocate patients to relevant treatment.

METHODS

We set out to investigate why it is so hard to identify (consistent) predictors of treatment outcome for psychotherapy in anxiety and depression by reviewing relevant literature.

RESULTS

Four challenges stand out; a) the complexity of human lives, b) sample size and statistical power, c) the complexity of therapist-patient relationships, and d) the lack of consistency in study designs. Together these challenges imply there are a countless number of possible predictors. We also consider ethical implications of predictor research in psychotherapy. Finally, we consider possible solutions, including the use of machine learning, larger samples and more realistic complex predictor models.

CONCLUSIONS

Our paper sheds light on why it is so hard to identify consistent predictors of treatment outcome in psychotherapy and suggest ethical implications as well as possible solutions to this problem.

Development of a model to predict psychotherapy response for depression among Veterans

BACKGROUND

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

METHODS

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

RESULTS

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

CONCLUSIONS

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

G-FORCE: the effectiveness of group psychotherapy for Cluster-C personality disorders: protocol of a pragmatic RCT comparing psychodynamic and two forms of schema group therapy

BACKGROUND

Cluster-C personality disorders (PDs), characterized by a high level of fear and anxiety, are related to high levels of distress, societal dysfunctioning and chronicity of various mental health disorders. Evidence for the optimal treatment is extremely scarce. Nevertheless, the need to treat these patients is eminent. In clinical practice, group therapy is one of the frequently offered approaches, with two important frameworks: schema therapy and psychodynamic therapy. These two frameworks suggest different mechanisms of change, but until now, this has not yet been explored. The purpose of the present G-FORCE trial is to find evidence on the differential (cost)effectiveness of two forms of schema group therapy and psychodynamic group therapy in the routine clinical setting of an outpatient clinic and to investigate the underlying working mechanisms and predictors of outcome of these therapies.

METHODS

In this mono-centre pragmatic randomized clinical trial, 290 patients with Cluster-C PDs or other specified PD with predominantly Cluster-C traits, will be randomized to one of three treatment conditions: group schema therapy for Cluster-C (GST-C, 1 year), schema-focused group therapy (SFGT, 1.5 year) or psychodynamic group therapy (PG, 2 years). Randomization will be pre-stratified on the type of PD. Change in severity of PD (APD-IV) over 24 months will be the primary outcome measure. Secondary outcome measures are personality functioning, psychiatric symptoms and quality of life. Potential predictors and mediators are selected and measured repeatedly. Also, a cost-effectiveness study will be performed, primarily based on a societal perspective, using both clinical effects and quality-adjusted life years. The time-points of assessment are at baseline, start of treatment and after 1, 3, 6, 9, 12, 18, 24 and 36 months.

DISCUSSION

This study is designed to evaluate the effectiveness and cost-effectiveness of three formats of group psychotherapy for Cluster-C PDs. Additionally, predictors, procedure and process variables are analysed to investigate the working mechanisms of the therapies. This is the first large RCT on group therapy for Cluster-C PDs and will contribute improving the care of this neglected patient group. The absence of a control group can be considered as a limitation.

TRIAL REGISTRATION

CCMO, NL72826.029.20 . Registered on 31 August 2020, first participant included on 18 October 2020.

Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

BACKGROUND

Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions.

METHODS

We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis.

RESULTS

Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians.

LIMITATIONS

Missing data limited the power to identify biological predictors of treatment outcome from certain modalities.

CONCLUSIONS

So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.

Individual psychotherapy for cluster-C personality disorders: protocol of a pragmatic RCT comparing short-term psychodynamic supportive psychotherapy, affect phobia therapy and schema therapy (I-FORCE)

BACKGROUND

Cluster-C personality disorders (PDs) are highly prevalent in clinical practice and are associated with unfavourable outcome and chronicity of all common mental health disorders (e.g. depression and anxiety disorders). Although several forms of individual psychotherapy are commonly offered in clinical practice for this population, evidence for differential effectiveness of different forms of psychotherapy is lacking. Also, very little is known about the underlying working mechanisms of these psychotherapies. Finding evidence on the differential (cost)-effectiveness for this group of patients and the working mechanisms of change is important to improve the quality of care for this vulnerable group of patients.

OBJECTIVE

In this study, we will compare the differential (cost)-effectiveness of three individual psychotherapies: short-term psychodynamic supportive psychotherapy (SPSP), affect phobia therapy (APT) and schema therapy (ST). Although these psychotherapies are commonly used in clinical practice, evidence for the Cluster-C PDs is limited. Additionally, we will investigate predictive factors, non-specific and therapy-specific mediators.

METHODS

This is a mono-centre randomized clinical trial with three parallel groups: (1) SPSP, (2) APT, (3) ST. Randomization on patient level will be pre-stratified according to type of PD. The total study population to be included consists of 264 patients with Cluster-C PDs or other specified PD with mainly Cluster-C traits, aged 18-65 years, seeking treatment at NPI, a Dutch mental health care institute specialized in PDs. SPSP, APT and ST (50 sessions per treatment) are offered twice a week in sessions of 50 min for the first 4 to 5 months. After that, session frequency decreases to once a week. All treatments have a maximum duration of 1 year. Change in the severity of the PD (ADP-IV) will be the primary outcome measure. Secondary outcome measures are personality functioning, psychiatric symptoms and quality of life. Several potential mediators, predictors and moderators of outcome are also assessed. The effectiveness study is complemented with a cost-effectiveness/utility study, using both clinical effects and quality-adjusted life-years, and primarily based on a societal approach. Assessments will take place at baseline, start of treatment and at 1, 3, 6, 9, 12, 18, 24 and 36 months.

DISCUSSION

This is the first study comparing psychodynamic treatment to schema therapy for Cluster-C PDs. The naturalistic design enhances the clinical validity of the outcome. A limitation is the lack of a control group for ethical reasons.

TRIAL REGISTRATION

NL72823.029.20 [Registry ID: CCMO]. Registered on 31 August 2020. First participant included on 23 October 2020.