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

Behavioural and nondirective parent training for children with externalising disorders: First steps towards personalised treatment recommendations

For children with externalising disorders, parent training programmes with different theoretical foundations are available. Currently, there is little knowledge concerning which programme should be recommended to a family based on their individual needs (e.g., single parenthood). The personalised advantage index (PAI) indicates the predicted treatment advantage of one treatment over another. The aim of the present study was to examine the usefulness of this score in providing individualised treatment recommendations. The analysis considered 110 parents (per-protocol sample) of children (4-11 years) with attention-deficit/hyperactivity (ADHD) or oppositional defiant disorder (ODD), randomised to either a behavioural or a nondirective telephone-assisted self-help parent training. In multiple moderator analyses with four different regression algorithms (linear, ridge, k-nearest neighbors, and tree), the linear model was preferred for computing the PAI. For ODD, families randomised to their PAI-predicted optimal intervention showed a treatment advantage of d = 0.54, 95% CI [0.17, 0.97]; for ADHD, the advantage was negligible at d = 0.35, 95% CI [-0.01, 0.78]. For children with conduct problems, it may be helpful if the PAI includes the treatment moderators single parent status and ODD baseline symptoms when providing personalised treatment recommendations for the selection of behavioural versus nondirective parent training. TRIAL REGISTRATION: The study was registered prospectively with ClinicalTrials.gov (Identifier NCT01350986).

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

BACKGROUND

Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA).

METHODS

A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample.

RESULTS

30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics.

LIMITATIONS

Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results.

CONCLUSIONS

A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Latent classes of symptom trajectories during partial hospitalization for major depressive disorder and generalized anxiety disorder

BACKGROUND

A variety of treatments have been empirically validated in the treatment of major depressive disorder and generalized anxiety disorder. Researchers commonly evaluate symptom change during treatment using single model curves, however, modeling multiple curves simultaneously allows for the identification of subgroups of patients that progress through treatment on distinct paths.

METHODS

Latent growth mixture modeling was used to identify and characterize distinct classes of symptom trajectories among two samples of patients with either MDD or GAD receiving treatment in a daily partial hospital program.

RESULTS

Four depression symptom trajectories were identified in the MDD sample, and three anxiety symptom trajectories were identified in the GAD sample. Both samples shared symptom trajectory classes of responders, rapid responders, and minimal responders, while the MDD sample demonstrated an additional class of early rapid responders. In both samples, low symptom severity at baseline was associated with membership in the responder class, though few other patterns emerged in baseline characteristics predicting trajectory class membership. At treatment discharge, those in the minimal responder class reported poorer outcomes on every clinical measure. Patients within each class reported similar scores at discharge as compared to each other class, indicating that class membership affects clinical measures beyond symptom severity.

LIMITATIONS

Patient demographic characteristics were relatively homogeneous. Group-based trajectory modeling inherently involves some degree of uncertainty regarding the number and shape of trajectories.

CONCLUSIONS

Identifying symptom trajectories can provide information regarding how patients are likely to progress through treatment, and thus inform clinicians when a patient deviates from expected progress.

Personalised psychotherapy in primary care: evaluation of data-driven treatment allocation to cognitive-behavioural therapy versus counselling for depression

BACKGROUND

Various effective psychotherapies exist for the treatment of depression; however, only approximately half of patients recover after treatment. In efforts to improve clinical outcomes, research has focused on personalised psychotherapy - an attempt to match patients to treatments they are most likely to respond to.

AIM

The present research aimed to evaluate the benefit of a data-driven model to support clinical decision-making in differential treatment allocation to cognitive-behavioural therapy versus counselling for depression.

METHOD

The present analysis used electronic healthcare records from primary care psychological therapy services for patients receiving cognitive-behavioural therapy (n = 14 544) and counselling for depression (n = 4725). A linear regression with baseline sociodemographic and clinical characteristics was used to differentially predict post-treatment Patient Health Questionnaire (PHQ-9) scores between the two treatments. The benefit of differential prescription was evaluated in a held-out validation sample.

RESULTS

On average, patients who received their model-indicated optimal treatment saw a greater improvement (by 1.78 PHQ-9 points). This translated into 4-10% more patients achieving clinically meaningful changes. However, for individual patients, the estimated differences in benefits of treatments were small and rarely met the threshold for minimal clinically important differences.

CONCLUSION

Precision prescription of psychotherapy based on sociodemographic and clinical characteristics is unlikely to produce large benefits for individual patients. However, the benefits may be meaningful from an aggregate public health perspective when applied at scale.

Do specific modules of cognitive behavioral therapy for depression have measurable effects on youth internalizing symptoms? An idiographic analysis

OBJECTIVE

Understanding the efficacy of each module of cognitive behavioral therapy (CBT) may inform efforts to improve outcomes for youth depression, but effects of specific modules have been difficult to examine. Idiographic interrupted time series models offer a robust way to estimate module effects on an individual's symptoms. This study examined the association of specific CBT modules for depression on internalizing symptoms among depressed youths who received modular CBT in a randomized trial.

METHODS

Individual models were created for three youths who met study criteria. Youths completed weekly symptom reports, and clinicians completed records of modules delivered. First order auto-regressive models quantified the change in average internalizing symptom severity between pre- and post-module delivery.

RESULTS

All youths had 1-3 modules that were significantly associated with symptom reduction and 1-3 modules associated with deterioration. The 5 modules associated with improvement in at least one youth also lacked association (engagement, relaxation, cognitive reframing), or were associated with worsening (activity selection, parent psychoeducation) in others. Seven modules showed no measurable benefit, or detriment to any youth.

CONCLUSION

This study demonstrated that specific modules have measurable effects, but more work is needed to build an evidence base of specific module effects to inform treatment personalization for youth depression.

Understanding mental health through computers: An introduction to computational psychiatry

Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

Evidence for distinct genetic and environmental influences on fear acquisition and extinction

BACKGROUND

Anxiety disorders are highly prevalent with an early age of onset. Understanding the aetiology of disorder emergence and recovery is important for establishing preventative measures and optimising treatment. Experimental approaches can serve as a useful model for disorder and recovery relevant processes. One such model is fear conditioning. We conducted a remote fear conditioning paradigm in monozygotic and dizygotic twins to determine the degree and extent of overlap between genetic and environmental influences on fear acquisition and extinction.

METHODS

In total, 1937 twins aged 22-25 years, including 538 complete pairs from the Twins Early Development Study took part in a fear conditioning experiment delivered remotely via the Fear Learning and Anxiety Response (FLARe) smartphone app. In the fear acquisition phase, participants were exposed to two neutral shape stimuli, one of which was repeatedly paired with a loud aversive noise, while the other was never paired with anything aversive. In the extinction phase, the shapes were repeatedly presented again, this time without the aversive noise. Outcomes were participant ratings of how much they expected the aversive noise to occur when they saw either shape, throughout each phase.

RESULTS

Twin analyses indicated a significant contribution of genetic effects to the initial acquisition and consolidation of fear, and the extinction of fear (15, 30 and 15%, respectively) with the remainder of variance due to the non-shared environment. Multivariate analyses revealed that the development of fear and fear extinction show moderate genetic overlap (genetic correlations 0.4-0.5).

CONCLUSIONS

Fear acquisition and extinction are heritable, and share some, but not all of the same genetic influences.

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.

The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.

Acceptance and commitment therapy and its unacknowledged influences: Some old wine in a new bottle?

Recently, Goldfried suggested that one main reason for the underdevelopment of psychotherapy as a scientific enterprise was the lack of acknowledgment of past contributions. In this article, this issue is illustrated by analysing the particular case of acceptance and commitment therapy (ACT). ACT has clear overlaps with therapies from the systemic tradition, such as strategic therapy in the line of the Mental Research Institute in Palo Alto and with the more recent models of solution-focused therapy and narrative therapy. This article analyses theoretical overlaps with these models (e.g. the paradoxical nature of human problems and the nature of language) as well as examples of similarities in therapeutic strategies (externalization and the miracle question). It concludes by suggesting that this practice of inadvertently obliterating the past does not favour the development of the field or the creation of consensus but rather contributes to the ongoing proliferation of 'new' psychotherapy models. Trends that may contribute to circumventing this problem are discussed.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Web-based training for post-secondary student well-being during the pandemic: a randomized trial

Background: The COVID-19 pandemic has been a highly stressful period where post-secondary education moved to online formats. Coping skills like decentering and reappraisal appear to promote stress resilience, but limited research exists on cultivating these skills in online learning contexts.Methods: In a three-arm randomized trial design, we evaluated three-week, web-based interventions to gauge how to best cultivate mindfulness and stress-reappraisal skills and whether the proposed interventions led to improved mental health. Undergraduate participants (N = 183) were randomly assigned to stress mindset, mindfulness meditation, or mindfulness with choice conditions.Results: At the study level (baseline vs. post-intervention), decentering improved across all conditions. Mindfulness with choice significantly decreased negative affect and rumination compared to stress mindset, while stress mindset significantly enhanced stress mindset skills compared to both mindfulness groups. At the daily level (three sessions per week), stress mindset significantly increased positive affect compared to mindfulness meditation.Conclusions: Results suggest that student mental health can be remotely supported through brief web-based interventions. Mindfulness practices seem to be effective in improving students' negative mood and coping strategies, while stress mindset training can help students to adopt a stress-is-enhancing mindset. Additional work on refining and better matching students to appropriate interventions is needed.

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention

OBJECTIVE

Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention.

METHODS

Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP).

RESULTS

Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r2 = 0.221, AUC = 0.77) and nighttime worry frequency (test r2 = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions.

LIMITATIONS

A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female.

CONCLUSIONS

This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.

Trans-diagnostic determinants of psychotherapeutic treatment response: The pressing need and new opportunities for a more systematic way of selecting psychotherapeutic treatment in the age of virtual service delivery

Numerous forms of psychotherapy have demonstrated effectiveness for individuals with specific mental disorders. It is, therefore, the task of the clinician to choose the most appropriate therapeutic approach for any given client to maximize effectiveness. This can prove to be a difficult task due to at least three considerations: (1) there is no treatment approach, method or model that works well on all patients, even within a particular diagnostic class; (2) several treatments are equally efficacious (i.e., more likely to be effective than no treatment at all) when considered only in terms of the patient's diagnosis; and (3) effectiveness in the real-world therapeutic setting is determined by a host of non-diagnostic factors. Typically, consideration of these latter, trans-diagnostic factors is unmethodical or altogether excluded from treatment planning - often resulting in suboptimal patient care, inappropriate clinic resource utilization, patient dissatisfaction with care, patient demoralization/hopelessness, and treatment failure. In this perspective article, we argue that a more systematic research on and clinical consideration of trans-diagnostic factors determining psychotherapeutic treatment outcome (i.e., treatment moderators) would be beneficial and - with the seismic shift toward online service delivery - is more feasible than it used to be. Such a transition toward more client-centered care - systematically considering variables such as sociodemographic characteristics, patient motivation for change, self-efficacy, illness acuity, character pathology, trauma history when making treatment choices - would result in not only decreased symptom burden and improved quality of life but also better resource utilization in mental health care and improved staff morale reducing staff burnout and turnover.

Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach

BACKGROUND

Early dropout hinders the effective adoption of brief psychological interventions and is associated with poor treatment outcomes. This study examined if attendance and depression treatment outcomes could be improved by matching patients to either face-to-face or computerized low-intensity psychological interventions.

METHODS

Archival clinical records were analysed for 85,664 patients who accessed face-to-face or computerized guided self-help (GSH). The primary outcome was early dropout (attending ≤3 sessions). Supervised machine learning analyses were applied in a training sample (n = 55,529). The trained algorithm was cross-validated in an independent test sample (n = 30,135). The clinical utility of the model was evaluated using logistic regression, chi-square tests, and sensitivity analyses in a balanced subsample.

RESULTS

Patients who received their model-indicated treatment modality were 12% more likely to receive an adequate dose of treatment OR = 1.12 (95% CI = 1.02 to 1.24), p = .02, and the strength of this effect was larger in the balanced subsample (OR = 2.10, 95% CI = 1.65 to 2.68, p < .001). Patients had better treatment outcomes when matched to their model-indicated treatment modality.

CONCLUSIONS

Machine learning approaches may enable services to optimally match patients to the treatment modality that maximizes attendance.

Patients' symptoms and strengths as predictors of long-term outcomes of CBT for generalized anxiety disorder - A three-level, multi-predictor analysis

Although cognitive behavioral therapy (CBT) is an effective treatment for generalized anxiety disorder (GAD), GAD often shows a chronic clinical course and common deterioration after treatment. Many trials have examined the efficacy of treatments in GAD, but little is known about intake predictors of long-term treatment outcomes. This study examined potential predictors of long-term treatment outcomes based on the individual's symptom severity and strengths (behavioral, cognitive, interpersonal) at intake. Long-term outcomes were defined as worry at six-month follow-up (six-m FU) and worry decrease from intake and post-treatment to six-m FU. Data from 137 CBT outpatients with a GAD diagnosis from two randomized clinical trials were analyzed using three-level hierarchical linear modeling. Results revealed that worrying decreased up to the six-m FU. In single-predictor models, intake symptom severity and strength measures predicted worry at the six-m FU. In multi-predictor models, only behavioral strengths remained a significant predictor. Worry decrease from intake to the six-m FU was only predicted by behavioral strengths. These findings provide relevant information about intake predictors of long-term outcomes after CBT for GAD and underscore the potential relevance of assessing patients' strengths for clinical practice.

Patient characteristics associated with retrospectively self-reported treatment outcomes following psychological therapy for anxiety or depressive disorders - a cohort of GLAD study participants

BACKGROUND

Progress towards stratified care for anxiety and depression will require the identification of new predictors. We collected data on retrospectively self-reported therapeutic outcomes in adults who received psychological therapy in the UK in the past ten years. We aimed to replicate factors associated with traditional treatment outcome measures from the literature.

METHODS

Participants were from the Genetic Links to Anxiety and Depression (GLAD) Study, a UK-based volunteer cohort study. We investigated associations between retrospectively self-reported outcomes following therapy, on a five-point scale (global rating of change; GRC) and a range of sociodemographic, clinical and therapy-related factors, using ordinal logistic regression models (n = 2890).

RESULTS

Four factors were associated with therapy outcomes (adjusted odds ratios, OR). One sociodemographic factor, having university-level education, was associated with favourable outcomes (OR = 1.37, 95%CI: 1.18, 1.59). Two clinical factors, greater number of reported episodes of illness (OR = 0.95, 95%CI: 0.92, 0.97) and higher levels of personality disorder symptoms (OR = 0.89, 95%CI: 0.87, 0.91), were associated with less favourable outcomes. Finally, reported regular use of additional therapeutic activities was associated with favourable outcomes (OR = 1.39, 95%CI: 1.19, 1.63). There were no statistically significant differences between fully adjusted multivariable and unadjusted univariable odds ratios.

CONCLUSION

Therapy outcome data can be collected quickly and inexpensively using retrospectively self-reported measures in large observational cohorts. Retrospectively self-reported therapy outcomes were associated with four factors previously reported in the literature. Similar data collected in larger observational cohorts may enable detection of novel associations with therapy outcomes, to generate new hypotheses, which can be followed up in prospective studies.

Personalized Prediction of Response to Smartphone-Delivered Meditation Training: Randomized Controlled Trial

BACKGROUND

Meditation apps have surged in popularity in recent years, with an increasing number of individuals turning to these apps to cope with stress, including during the COVID-19 pandemic. Meditation apps are the most commonly used mental health apps for depression and anxiety. However, little is known about who is well suited to these apps.

OBJECTIVE

This study aimed to develop and test a data-driven algorithm to predict which individuals are most likely to benefit from app-based meditation training.

METHODS

Using randomized controlled trial data comparing a 4-week meditation app (Healthy Minds Program [HMP]) with an assessment-only control condition in school system employees (n=662), we developed an algorithm to predict who is most likely to benefit from HMP. Baseline clinical and demographic characteristics were submitted to a machine learning model to develop a "Personalized Advantage Index" (PAI) reflecting an individual's expected reduction in distress (primary outcome) from HMP versus control.

RESULTS

A significant group × PAI interaction emerged (t658=3.30; P=.001), indicating that PAI scores moderated group differences in outcomes. A regression model that included repetitive negative thinking as the sole baseline predictor performed comparably well. Finally, we demonstrate the translation of a predictive model into personalized recommendations of expected benefit.

CONCLUSIONS

Overall, the results revealed the potential of a data-driven algorithm to inform which individuals are most likely to benefit from a meditation app. Such an algorithm could be used to objectively communicate expected benefits to individuals, allowing them to make more informed decisions about whether a meditation app is appropriate for them.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04426318; https://clinicaltrials.gov/ct2/show/NCT04426318.

A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression. We conducted a formal comparison of a Markov decision process approach and mainstream state-transition models as used in health economic decision analysis to clarify differences in the model structure. We performed two reviews: the first to identify existing applications of the Markov decision process in the field of healthcare and the second to identify existing health economic models for depression. We then illustrated the application of a Markov decision process by reformulating an existing health economic model. This provided input for discussing the suitability of a Markov decision process for solving sequential treatment decisions in depression. The Markov decision process and state-transition models differed in terms of flexibility in modeling actions and rewards. In all, 23 applications of a Markov decision process within the context of somatic disease were included, 16 of which concerned sequential treatment decisions. Most existing health economic models relating to depression have a state-transition structure. The example application replicated the health economic model and enabled additional capacity to make dynamic comparisons of more interventions over time than was possible with traditional state-transition models. Markov decision processes have been successfully applied to address sequential treatment-decision problems, although the results have been published mostly in economics journals that are not related to healthcare. One advantage of a Markov decision process compared with state-transition models is that it allows extended action space: the possibility of making dynamic comparisons of different treatments over time. Within the context of depression, although existing state-transition models are too basic to evaluate sequential treatment decisions, the assumptions of a Markov decision process could be satisfied. The Markov decision process could therefore serve as a powerful model for optimizing sequential treatment in depression. This would require a sufficiently elaborate state-transition model at the cohort or patient level.

Exploring personalized psychotherapy for depression: A system dynamics approach

Depressive disorders are the leading contributor to medical disability, yet only 22% of depressed patients receive adequate treatment in a given year. Response to treatment varies widely among individuals with depression, and poor response to one treatment does not signal poor response to others. In fact, half of patients who do not recover from a first-line psychotherapy will recover from a second option. Attempts to personalize psychotherapy to patient characteristics have produced better outcomes than usual care, but research on personalized psychotherapy is still in its infancy. The present study explores a new method for personalizing psychotherapy for depression through simulation modeling. In this study, we developed a system dynamics simulation model of depression based on one of the major mechanisms of depression in the literature and investigated the trend of depressive symptoms under different conditions and treatments. Our simulation outputs show the importance of individualized services with appropriate timing, and reveal a new method for personalizing psychotherapy to heterogeneous individuals. Future research is needed to expand the model to include additional mechanisms of depression.

Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study

Background

Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms.

Objective

To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.

Method

The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.

Results

Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.

Conclusions

In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.

Trial Registry Name

Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov.

Design of an RCT on cost-effectiveness of group schema therapy versus individual schema therapy for patients with Cluster-C personality disorder: the QUEST-CLC study protocol

BACKGROUND

Given the high prevalence of Cluster-C Personality Disorders (PDs) in clinical populations, disease burden, high societal costs and poor prognosis of comorbid disorders, a major gain in health care can be achieved if Cluster-C PDs are adequately treated. The only controlled cost-effectiveness study published so far found Individual Schema Therapy (IST) to be superior to Treatment as Usual (TAU). Group ST (GST) might improve cost-effectiveness as larger numbers can be treated in (>50%) less time compared to IST. However, to date there is no RCT supporting its (cost-) effectiveness. The overall aim of this study is to assess the evidence for GST for Cluster-C PDs and to improve treatment allocation for individual patients. Three main questions are addressed: 1) Is GST for Cluster-C PDs (cost-)effective compared to TAU? 2) Is GST for Cluster-C PDs (cost-) effective compared to IST? 3) Which patient-characteristics predict better response to GST, IST, or TAU?

METHODS

In a multicenter RCT, the treatment conditions GST, IST, and TAU are compared in 378 Cluster-C PD patients within 10 sites. GST and IST follow treatment protocols and are completed within 1 year. TAU is the optimal alternative treatment available at the site according to regular procedures. Severity of the Cluster-C PD is the primary outcome, assessed with clinical interviews by independent raters blind for treatment. Functioning and wellbeing are important secondary outcomes. Assessments take place at week 0 (baseline), 17 (mid-GST), 34 (post-GST), 51 (post-booster sessions of GST), and 2 years (FU). Patient characteristics predicting better response to a specific treatment are studied, e.g., childhood trauma, autistic features, and introversion. A tool supporting patients and clinicians in matching treatment to patient will be developed. An economic evaluation investigates the cost-effectiveness and cost-utility from a societal perspective. A process evaluation by qualitative methods explores experiences of participants, loved ones and therapists regarding recovery, quality of life, and improving treatment.

DISCUSSION

This study will determine the (cost-)effectiveness of treatments for Cluster-C PDs regarding treatment type as well as optimal matching of patient to treatment and deliver insight into which aspects help Cluster-C-PD patients recover and create a fulfilling life.

TRIAL REGISTRATION

Dutch Trial Register: NL9209 . Registered on 28-01-2021.

Is it worth it to personalize the treatment of PTSD? - A variance-ratio meta-analysis and estimation of treatment effect heterogeneity in RCTs of PTSD

Several evidence-based treatments for posttraumatic stress disorder (PTSD) are recommended by international guidelines (e.g., APA, NICE). While their average effects are in general high, non-response rates indicate differential treatment effects. Here, we used a large database of RCTs on psychotherapy for PTSD to determine a reliable estimate of this heterogeneity in treatment effects (HTE) by applying Bayesian variance ratio meta-analysis. In total, 66 studies with a total of 8803 patients were included in our study. HTE was found for all psychological treatments, with varying degrees of certainty, only slight differences between psychological treatments, and active control groups yielding a smaller variance ratio compared to waiting list control groups. Across all psychological treatment and control group types, the estimate for the intercept was 0.12, indicating a 12% higher variance of posttreatment values in the intervention groups after controlling for differences in treatment outcomes. This study is the first to determine the maximum increase in treatment effects of psychological treatments for PTSD by personalization. The results indicate that there is comparatively high heterogeneity in treatment effects across all psychological treatment and control groups, which in turn allow personalizing psychological treatments by using treatment selection approaches.

Acute and chronic stress predict anti-depressant treatment outcome and naturalistic course of major depression: A CAN-BIND report

BACKGROUND

In treatment studies of major depressive disorder (MDD), exposure to major life events predicts less symptom improvement and greater likelihood of relapse. In contrast, the impact of minor life events has received less attention. We hypothesized that the impact of minor events on symptom improvement and risk of relapse would be heightened in the presence of concurrent chronic stress. We also hypothesized that major events would predict less symptom improvement and greater risk of relapse independently of chronic stress.

METHODS

Adult patients experiencing an episode of MDD were enrolled into a 16-week trial with antidepressant treatments (n = 156). Forty-three fully remitted patients agreed to participate in a naturalistic 18-month follow-up, and 30 had full data for analyses. Life events and chronic stressors were assessed using a contextual life stress interview.

RESULTS

Greater exposure to minor events predicted greater improvement in symptoms during acute treatment, but this relation was specific to those who reported greater severity of chronic stress. During follow-up, however, major life events predicted increased risk of relapse, and this effect was not moderated by chronic stress.

LIMITATION

High attrition rates led to a small sample size for the follow-up analyses.

CONCLUSIONS

Exposure to minor events may provide an opportunity to practice problem-solving skills, thereby facilitating symptom improvement. Nevertheless, acute treatment did not protect patients from relapse when they subsequently faced major events during follow-up. Therefore, adjunctive strategies may be needed to enhance outcomes during pharmacotherapy, consolidating benefits from acute treatment and providing skills to prevent relapse.

Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid

OBJECTIVE

To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).

DESIGN

A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.

SETTING

Dutch primary care from 2015, with social media included from 2017. Enrolment ended on July 2018.

PARTICIPANTS

Adult women were eligible if they had ≥2 episodes of UI per week, access to mobile apps and wanted treatment. Of the 350 screened women, 262 were eligible and randomised to app-based treatment or care as usual; 195 (74%) attended follow-up.

PREDICTORS

Literature review and expert opinion identified 13 candidate predictors, categorised into two groups: Prognostic factors (independent of treatment type), such as UI severity, postmenopausal state, vaginal births, general physical health status, pelvic floor muscle function and body mass index; and modifiers (dependent on treatment type), such as age, UI type and duration, impact on quality of life, previous physical therapy, recruitment method and educational level.

MAIN OUTCOME MEASURE

Primary outcome was symptom severity after a 4-month follow-up period, measured by the International Consultation on Incontinence Questionnaire the Urinary Incontinence Short Form. Prognostic factors and modifiers were combined into a final prediction model. For each participant, we then predicted treatment outcomes and calculated a Personalised Advantage Index (PAI).

RESULTS

Baseline UI severity (prognostic) and age, educational level and impact on quality of life (modifiers) independently affected treatment effect of eHealth. The mean PAI was 0.99±0.79 points, being of clinical relevance in 21% of individuals. Applying the PAI also significantly improved treatment outcomes at the group level.

CONCLUSIONS

Personalising treatment choice can support treatment decision making between eHealth and care as usual through the practical application of prediction modelling. Concerning eHealth for UI, this could facilitate the choice between app-based treatment and care as usual.

TRIAL REGISTRATION NUMBER

NL4948t.

A comparison of prolonged exposure therapy, pharmacotherapy, and their combination for PTSD: What works best and for whom; study protocol for a randomized trial

BACKGROUND

Several efficacious psychological and pharmacological treatments for posttraumatic stress disorder (PTSD) are available; however, the comparative effectiveness of these treatments represents a major gap in the literature. The proposed study will compare the effectiveness of two leading PTSD treatments - Prolonged Exposure (PE) therapy and pharmacotherapy with paroxetine or venlafaxine extended release - as well as the combination of PE and medication.

METHODS

In a randomized clinical trial, veterans with PTSD (N = 450) recruited across six Veterans Affairs Medical Centers will complete assessments at baseline, mid-treatment (Week 7), post-treatment (Week 14), and follow-up (Weeks 27 and 40). The primary outcome will be change in (both clinician-rated and self-reported) PTSD severity. Depression symptoms, quality of life, and functioning will also be measured and examined as secondary outcomes. Baseline demographic and clinical data will be used to develop "personalized advantage indices" (PAIs), with the goal of identifying who is most likely to benefit from which treatment.

CONCLUSIONS

This planned trial will yield findings to directly inform clinical practice guidelines for PTSD, by providing comparative effectiveness data to support recommendations about what can be considered the "first-line" treatment option(s) for PTSD. Further, findings from this trial have the potential to guide treatment planning for individual patients, through implementation of PAIs developed from study data, in service of "personalized medicine."

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT04961190.

Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.

Factors Associated with Increased Health-Related Quality of Life Benefits in Patients with Hereditary Transthyretin Amyloidosis with Polyneuropathy (ATTRv-PN) Treated with Inotersen

INTRODUCTION/AIMS

Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) is a genetic condition associated with significant morbidity and mortality. We aimed to identify patient subgroups exhibiting the greatest health-related quality of life [HRQL] benefit from inotersen treatment.

METHODS

We examined data from the inotersen phase 2/3 randomized controlled trial for ATTRv-PN, NEURO-TTR (NCT01737398, 66 weeks). LASSO regression models predicted changes in Norfolk QoL-DN total score (TQoL, range -4 to 136; higher scores indicate poorer HRQL) from baseline in the inotersen and placebo arm, respectively. Individualized efficacy scores (ES) were calculated as differences between predicted change-scores had patients received inotersen vs. placebo. Patients were ranked by ES to define the greatest-benefit subpopulation (top 50%). Characteristics of the top 50% and bottom 50% of patients were compared.

RESULTS

The overall mean ± standard deviation TQoL change was -0.20±19.13 for inotersen (indicating no change) and 10.77±21.13 for placebo (indicating deterioration). Within the highest-benefit patients, mean TQoL change was -11.03±17.06 (improvement) for inotersen and 11.24±22.97 (deterioration) for placebo (P<0.001). Compared with the overall population, patients in the greatest-benefit subpopulation were younger, more likely to have polyneuropathy disability (PND) scores 1 or 2, less likely to have received prior tafamidis or diflunisal treatment, and more likely to have Val30Met mutations and higher (worse) baseline TQoL.

CONCLUSION

Patients who were younger and/or at earlier polyneuropathy stages experienced greater HRQL benefits from inotersen over 66 weeks. These findings underscore the need for early diagnosis and treatment initiation, especially among more severely affected patients in early stages of ATTRv-PN. This article is protected by copyright. All rights reserved.

The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems.

METHODS

Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE.

DISCUSSION

The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019.

Study protocol for pragmatic trials of Internet-delivered guided and unguided cognitive behavior therapy for treating depression and anxiety in university students of two Latin American countries: the Yo Puedo Sentirme Bien study

BACKGROUND

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective.

METHODS

We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms.

DISCUSSION

By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation.

TRIAL STATUS

IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.

Inpatient psychotherapy for depression in a large routine clinical care sample: A Bayesian approach to examining clinical outcomes and predictors of change

BACKGROUND

A routinely collected dataset was analyzed (1) to determine the naturalistic effectiveness of inpatient psychotherapy for depression in routine psychotherapeutic care, and (2) to identify potential predictors of change.

METHODS

In a sample of 22,681 inpatients with depression, pre-post and pre-follow-up effect sizes were computed for various outcome variables. To build a probabilistic model of predictors of change, an independent component analysis generated components from demographic and clinical data, and Bayesian EFA extracted factors from the available pre-test, post-test and follow-up questionnaires in a subsample (N = 6377). To select the best-fitted model, the BIC of different path models were compared. A Bayesian path analysis was performed to identify the most important factors to predict changes.

RESULTS

Effect sizes were large for the primary outcome and moderate for various secondary outcomes. Almost all pretreatment factors exerted significant influences on different baseline factors. Several factors were found to be resistant to change during treatment: suicidality, agoraphobia, life dissatisfaction, physical disability and pain. The strongest cross-loadings were observed from suicidality on negative cognitions, from agoraphobia on anxiety, and from physical disability on perceived disability.

LIMITATIONS

No causal conclusions can be drawn directly from our results as we only used cross-lagged panel data without control group.

CONCLUSIONS

The results indicate large effects of inpatient psychotherapy for depression in routine clinical care. The direct influence of pretreatment factors decreased over the course of treatment. However, some factors appeared stable and difficult to treat, which might hinder treatment outcome. Findings of different predictors of change are discussed.

The Development of Psychosocial Therapeutic and Preventive Interventions for Mental Disorders (R61/R33): A User's Guide

One of the four major goals outlined in the National Institute of Mental Health (NIMH) strategic plan (2021) is to develop and test new treatments and prevention strategies. The aim of the Funding Opportunity Announcement (FOA) for the R61/R33 grant mechanism has been to support the efficient pilot testing of exploratory clinical trials of novel interventions for mental disorders in adults and children through an experimental therapeutics approach. The present commentary (a) describes the R61/R33 grant mechanism, defines terms, and summarizes information about current grants in the system, (b) outlines the review criteria, and (c) highlights several common critiques. Frequent concerns expressed by applicants as well as reviewers include defining and measuring the target/mechanism, establishing dose, selecting an appropriate control group, measuring fidelity, and determining power. Finally, alternative pathways for conducting randomized clinical trials for intervention development are discussed in contrast to or in addition to the experimental therapeutics approach for discovering novel interventions aimed at reducing and preventing mental illness across the lifespan.

Praxisorientierte Forschung in der Psychotherapie

Zusammenfassung. In den letzten Jahrzehnten hat sich durch randomisiert-kontrollierte Studien (RCTs) eine breite Evidenzbasis von Psychotherapie mit mittleren bis großen Effekten für verschiedene psychische Störungen gebildet. Neben der Bestimmung dieser Wirksamkeit („Efficacy“) ebneten Studien zur Wirksamkeit unter alltäglichen Routinebedingungen („Effectiveness“) historisch den Weg zur Entwicklung eines praxisorientierten Forschungsparadigmas. Im Beitrag wird argumentiert, dass im Rahmen dieses Paradigmas praxisbasierte Studien eine wertvolle Ergänzung zu RCTs darstellen, da sie existierende Probleme in der Psychotherapieforschung adressieren können. In der gegenwärtigen praxisorientierten Forschung liefern dabei neue Ansätze aus der personalisierten Medizin und Methoden aus der ‚Computational Psychiatry‘ wichtige Anhaltspunkte zur Optimierung von Effekten in der Psychotherapie. Im Kontext der Personalisierung werden bspw. klinische multivariable Prädiktionsmodelle entwickelt, welche durch Rückmeldeschleifen an Praktiker_innen kurzfristig ein evidenzbasiertes Outcome-Monitoring ermöglicht und langfristig das Praxis-Forschungsnetzwerk in Deutschland stärkt. Am Ende des Beitrags werden zukünftige Richtungen für die praxisorientierte Forschung im Sinne des ‘Precision Mental Health Care’ -Paradigmas abgeleitet und diskutiert.

Somatosensory amplification moderates the efficacy of internet-delivered CBT for somatic symptom distress in emerging adults: Exploratory analysis of a randomized controlled trial

OBJECTIVE

While studies mainly provide positive evidence for the efficacy of internet-delivered cognitive-behavioral therapy (ICBT) for various persistent somatic symptoms, it remains largely unclear for whom these interventions work or not. This exploratory analysis aimed to identify moderators for the outcome between ICBT for somatic symptom distres and a waitlist control group (WL) in a vulnerable target group of emerging adults.

METHODS

Based on data from a randomized controlled trial on 156 university students with varying degrees of somatic symptom distress who were allocated to either an eight-week, therapist guided ICBT (iSOMA) or to the WL, we examined pretreatment demographic characteristics, health-related variables (e.g., somatic symptom duration), mental distress (e.g., depression, anxiety) and cognitive-emotional factors (emotional reactivity, somatosensory amplification) as candidate moderators of the outcome, somatic symptom distress (assessed by the Patient Health Questionnaire, PHQ-15) from pre- to posttreatment.

RESULTS

Somatosensory amplification (assessed by the Somatosensory Amplification Scale, SSAS) moderated the outcome in favor of iSOMA (B = -0.17, SE = 0.08, p = 0.031), i.e., higher pretreatment somatosensory amplification was associated with better outcome in the active compared to the control intervention. No significant moderation effects were found among demographic characteristics, health-related variables, or mental distress.

CONCLUSION

Our findings suggest that an internet-delivered CBT for somatic symptom distress should be preferred over no active treatment particularly in individuals with moderate to high levels of somatosensory amplification, which as a next step should be tested against further treatments and in clinical populations.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00014375).

How does early symptom change predict subsequent course of depressive symptoms during psychotherapy?

OBJECTIVES

Previous studies reported about the influence of early changes on treatment response. However, the question of whether early changes can predict the subsequent course of depressive symptoms during treatment with psychotherapy has not yet been clearly answered. We aimed to investigate whether symptom course in the first weeks at the level of individual session can predict the further symptom progression on a session to session level during psychotherapy treatment in patients with Major Depression (MD).

DESIGN

Monocentric randomized controlled trial with psychotherapeutic treatment either with cognitive-behavioural therapy (CBT) or hypnotherapy (HT). The longitudinal course of weekly depressive symptoms during the six months treatment period was examined.

METHODS

In this RCT with 152 randomized patients suffering from current mild-to-moderate MD, depressive symptoms were assessed on a weekly basis during the 20 sessions' treatment with individual psychotherapy. We only included patients for which sufficient data for our analysis were available. Three different linear and quadratic mixed model analyses with random effects for each patient were tested: Early change was defined as the individual percentage symptom change during the first two, three, four and five weeks. Symptoms from session four, five, six and seven onward were predicted using different models, with early change added to the model in a final step. Calculating all models separately for CBT and HT lead to comparable results.

RESULT

A slow symptom decrease after session four, five, six, seven onward to the end of the treatment was found. However, adding early change to the model, had no effect on the further symptom course in all models.

CONCLUSION

Symptom changes at early stages of psychotherapy should not be considered as being predictive for further symptom course.

PRACTITIONER POINTS

The individual early symptom change in a treatment with psychotherapy in the first two, three, four, or five weeks of treatment does not predict the subsequent symptom course from session four, five, six, or seven onward at a session to session level. Symptom changes at early stages of psychotherapy should not be considered as being predictive for further symptom course. We found a symptom reduction ranging from 3% to 16% in the first two, three, four, or five weeks. Treatment response between the first and last therapy session was found in 54.5%, the number of remitted patients (with PHQ-9 scores < 5) was 44.7%. A small symptom improvement of between 0.21 and 0.42 points in the PHQ-9 scores per week in later stages of psychotherapy is likely in all patients (with and without early symptom improvement).

Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy

BACKGROUND

Specialized evidence-based treatments have been developed and evaluated for borderline personality disorder (BPD), including Dialectical Behavior Therapy (DBT) and Schema Therapy (ST). Individual differences in treatment response to both ST and DBT have been observed across studies, but the factors driving these differences are largely unknown. Understanding which treatment works best for whom and why remain central issues in psychotherapy research. The aim of the present study is to improve treatment response of DBT and ST for BPD patients by a) identifying patient characteristics that predict (differential) treatment response (i.e., treatment selection) and b) understanding how both treatments lead to change (i.e., mechanisms of change). Moreover, the clinical effectiveness and cost-effectiveness of DBT and ST will be evaluated.

METHODS

The BOOTS trial is a multicenter randomized clinical trial conducted in a routine clinical setting in several outpatient clinics in the Netherlands. We aim to recruit 200 participants, to be randomized to DBT or ST. Patients receive a combined program of individual and group sessions for a maximum duration of 25 months. Data are collected at baseline until three-year follow-up. Candidate predictors of (differential) treatment response have been selected based on the literature, a patient representative of the Borderline Foundation of the Netherlands, and semi-structured interviews among 18 expert clinicians. In addition, BPD-treatment-specific (ST: beliefs and schema modes; DBT: emotion regulation and skills use), BPD-treatment-generic (therapeutic environment characterized by genuineness, safety, and equality), and non-specific (attachment and therapeutic alliance) mechanisms of change are assessed. The primary outcome measure is change in BPD manifestations. Secondary outcome measures include functioning, additional self-reported symptoms, and well-being.

DISCUSSION

The current study contributes to the optimization of treatments for BPD patients by extending our knowledge on "Which treatment - DBT or ST - works the best for which BPD patient, and why?", which is likely to yield important benefits for both BPD patients (e.g., prevention of overtreatment and potential harm of treatments) and society (e.g., increased economic productivity of patients and efficient use of treatments).

TRIAL REGISTRATION

Netherlands Trial Register, NL7699 , registered 25/04/2019 - retrospectively registered.

The Use of Information and Communication Technologies in Family Support across Europe: A Narrative Review

The COVID-19 pandemic has accelerated the use of information and communication technology (ICT) to deliver parenting and mental health support services to families. This narrative review illustrates the diverse ways in which ICT is being used across Europe to provide family support to different populations. We distinguish between the use of ICT in professional-led and peer-led support and provide implementation examples from across Europe. We discuss the potential advantages and disadvantages of different ways of using ICT in family support and the main developments and challenges for the field more generally, guiding decision-making as to how to use ICT in family support, as well as critical reflections and future research on its merit.

Personalized prescriptions of therapeutic skills from patient characteristics: An ecological momentary assessment approach

OBJECTIVE

Rather than relying on a single psychotherapeutic orientation, most clinicians draw from a range of therapeutic approaches to treat their clients. To date, no data-driven approach exists for personalized predictions of which skill domain would be most therapeutically beneficial for a given patient. The present study combined ecological momentary assessment (EMA) and machine learning to test a data-driven approach for predicting patient-specific skill-outcome associations.

METHOD

Fifty (M_age = 37 years old, 54% female, 84% White) adults received training in behavioral therapy (BT) and dialectical behavior therapy (DBT) skills within a behavioral health partial hospital program (PHP). Following discharge, patients received four EMA surveys per day for 2 weeks (total observations = 2,036) assessing the use of therapeutic skills and positive/negative affect (PA/NA). Clinical and demographic characteristics were submitted to elastic net regularization to predict, via cross-validation, patient-specific associations between the use of BT versus DBT skills and level of PA/NA.

RESULTS

Cross-validated accuracy was 81% (sensitivity = 93% and specificity = 63%) in predicting whether a patient would exhibit a stronger association between the use of BT versus DBT skills and PA level. Predictors of positive DBT skills-PA associations included higher levels of nonsuicidal self-injury (NSSI) and sleep disturbance, whereas predictors of positive BT skills-PA relations included higher emotional lability and anxiety disorder comorbidity, and lower psychomotor retardation/agitation and worthlessness/guilt. Corresponding models with NA yielded no predictors.

CONCLUSIONS

Findings from this initial proof-of-concept study highlight the potential of data-driven approaches to inform personalized prescriptions of which skill domains may be most therapeutically beneficial for a given patient. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Older adults respond better to psychological therapy than working-age adults: evidence from a large sample of mental health service attendees

BACKGROUND

Older adults commonly experience depression and anxiety, yet are under-represented in psychological treatment services. There is uncertainty about the outcomes from psychological therapies for older adults relative to working-age adults. This study explored: pre-treatment differences between older and working-age patients with depression or anxiety disorders; whether outcomes from psychological therapy differ between groups controlling for pre-treatment clinical severity, functioning, and socio-demographics; and whether the impact of a long-term health condition (LTC) on outcome differs by age.

METHODS

Data on >100,000 patients treated with psychological therapies in eight Improving Access to Psychological Therapies services were analyzed. We compared pre-treatment characteristics and therapy outcomes for older (≥65 years) and working-age (18-64 years) patients, and investigated associations between age and outcomes.

RESULTS

Older adults had less severe clinical presentations pre-treatment. In adjusted models older adults were more likely to reliably recover (OR=1.33(95%CI=1.24-1.43)), reliably improve (OR=1.34(95%CI =1.24-1.45)), and attrition was less likely (OR=0.48(95%CI =0.43-0.53)). Effects were more pronounced in patients with anxiety disorders compared to depression. Having an LTC was associated with a much lower likelihood of reliable recovery for working-age patients but had only a modest effect for older adults.

LIMITATIONS

There are potential selection biases affecting the characteristics of older people attending these services. Residual confounding cannot be ruled out due to limits on data available.

CONCLUSIONS

Older adults experienced better outcomes from psychological treatments than working-age adults. Given the deleterious effects if mental health conditions go untreated, increasing access to psychological therapies for older people should be an international priority.

Moderators of response to child-based and parent-based child anxiety treatment: a machine learning-based analysis

BACKGROUND

Identifying moderators of response to treatment for childhood anxiety can inform clinical decision-making and improve overall treatment efficacy. We examined moderators of response to child-based cognitive-behavioral therapy (CBT) and parent-based SPACE (Supportive Parenting for Anxious Childhood Emotions) in a recent randomized clinical trial.

METHODS

We applied a machine learning approach to identify moderators of treatment response to CBT versus SPACE, in a clinical trial of 124 children with primary anxiety disorders. We tested the clinical benefit of prescribing treatment based on the identified moderators by comparing outcomes for children randomly assigned to their optimal and nonoptimal treatment conditions. We further applied machine learning to explore relations between moderators and shed light on how they interact to predict outcomes. Potential moderators included demographic, socioemotional, parenting, and biological variables. We examined moderation separately for child-reported, parent-reported, and independent-evaluator-reported outcomes.

RESULTS

Parent-reported outcomes were moderated by parent negativity and child oxytocin levels. Child-reported outcomes were moderated by baseline anxiety, parent negativity, and parent oxytocin levels. Independent-evaluator-reported outcomes were moderated by baseline anxiety. Children assigned to their optimal treatment condition had significantly greater reduction in anxiety symptoms, compared with children assigned to their nonoptimal treatment. Significant interactions emerged between the identified moderators.

CONCLUSIONS

Our findings represent an important step toward optimizing treatment selection and increasing treatment efficacy.

Personalized Depression Prevention: A Randomized Controlled Trial to Optimize Effects Through Risk-Informed Personalization

OBJECTIVE

To evaluate whether evidence-based depression prevention programs can be optimized by matching youths to interventions that address their psychosocial vulnerabilities.

METHOD

This randomized controlled trial included 204 adolescents (mean [SD] age = 14.26 [1.65] years; 56.4% female). Youths were categorized as high or low on cognitive and interpersonal risks for depression and randomly assigned to Coping With Stress (CWS), a cognitive-behavioral program, or Interpersonal Psychotherapy-Adolescent Skills Training (IPT-AST), an interpersonal program. Some participants received a match between risk and prevention (eg, high cognitive-low interpersonal risk teen in CWS, low cognitive-high interpersonal risk teen in IPT-AST), others received a mismatch (eg, low cognitive-high interpersonal risk teen in CWS). Outcomes were depression diagnoses and symptoms through 18 months postintervention (21 months total).

RESULTS

Matched adolescents showed significantly greater decreases in depressive symptoms than mismatched adolescents from postintervention through 18-month follow-up and across the entire 21-month study period (effect size [d] = 0.44, 95% CI = 0.02, 0.86). There was no significant difference in rates of depressive disorders among matched adolescents compared with mismatched adolescents (12.0% versus 18.3%, t₁₉₃ = .78, p = .44).

CONCLUSION

This study illustrates one approach to personalizing depression prevention as a form of precision mental health. Findings suggest that risk-informed personalization may enhance effects beyond a one-size-fits-all approach.

CLINICAL TRIAL REGISTRATION INFORMATION

Bending Adolescent Depression Trajectories Through Personalized Prevention; https://www.clinicaltrials.gov/; NCT01948167.

Psychometric Properties of the German Version of the Young Positive Schema Questionnaire (YPSQ) in the General Population and Psychiatric Patients

Early adaptive schemas (EAS) are resilience-oriented counterparts to early maladaptive schemas (EMS), which are central in schema therapy. The Young Positive Schema Questionnaire (YPSQ) was developed as a measure of EAS but has been evaluated neither in relation to a clinical population nor in a German-speaking sample. Objectives of this study were therefore the psychometric validation of a German YPSQ in a community sample and the comparison of EAS to psychiatric patients. Participants were 1,418 individuals from a community sample and 182 psychiatric patients with a main diagnosis of major depressive disorder. A factor structure of 10 EAS, instead of the original 14, demonstrated satisfactory factorial validity and internal consistency in both samples. EAS exhibited divergent validity to EMS, childhood trauma, and psychopathology. Convergent validity was evident with resilience, self-efficacy, and satisfaction with life. Support for incremental validity beyond EMS was especially shown for resilience, self-efficacy, and satisfaction with life, and was also evident for several dimensions of psychopathology. Individuals in the community sample exhibited more pronounced EAS compared to psychiatric patients with the exception of empathic consideration. Especially for concepts associated with mental health, the YPSQ has the potential to be a highly valuable addition to current research and practice.