Different people respond differently to therapy: A demonstration using patient profiling and risk stratification

First impressions matter: The influence of initial assessments on psychological treatment initiation and subsequent dropout

OBJECTIVE

This study investigated if patients' experience of an initial assessment may be associated with outcome expectations, and with subsequent treatment attendance.

METHOD

The sample comprised n = 6051 patients with depression/anxiety disorders, nested within k = 148 assessing therapists. Multilevel modelling (MLM) was used to examine therapist effects on treatment initiation and subsequent dropout, adjusting for patient-level characteristics. We tested associations between early outcome expectancy measured at an initial assessment with attendance at a first therapy session, and with dropout after initiation. Variability in mean expectancy ratings in the caseloads of assessing therapists was examined using the intracluster correlation coefficient (ICC).

RESULTS

Therapist effects partly explained the variance in treatment initiation and dropout. Pre-treatment outcome expectations significantly predicted treatment initiation but not dropout for the subgroup of patients who started treatment. Approximately 16% of variability in mean expectancy ratings was explained by therapist effects (ICC = 0.159) after controlling for patient-level covariates.

CONCLUSIONS

Patients assessed by some therapists are more likely to have higher outcome expectations, which influences their decision to initiate treatment thereafter. Once patients start therapy, early expectancy measured at assessment no longer influences their attendance, but the "first impression" from an initial assessment does influence their subsequent likelihood of dropout.

Statistical methods to adjust for the effects on intervention compliance in randomized clinical trials where precision treatment rules are being developed

BACKGROUND

Heterogeneity of treatment effects (HTEs) can occur because of either differential treatment compliance or differential treatment effectiveness. This distinction is important, as it has action implications, but it is unclear how to distinguish these two possibilities statistically in precision treatment analysis given that compliance is not observed until after randomization. We review available statistical methods and illustrate a recommended method in secondary analysis in a trial focused on HTE.

METHODS

The trial randomized n = 880 anxious and/or depressed university students to guided internet-delivered cognitive behavioral therapy (i-CBT) or treatment-as-usual (TAU) and evaluated joint remission. Previously reported analyses documented superiority of i-CBT but significant HTE. In the reanalysis reported here, we used baseline (i.e., pre-randomization) covariates to predict compliance among participants randomized to guided i-CBT, generated a cross-validated within-person expected compliance score based on this model in both intervention groups, and then used this expected composite score as a predictor in an expanded HTE analysis.

RESULTS

The significant intervention effect was limited to participants with high expected compliance. Residual HTE was nonsignificant.

CONCLUSIONS

Future psychotherapy HTE trials should routinely develop and include expected compliance composite scores to distinguish the effects of differential treatment compliance from the effects of differential treatment effectiveness.

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.

Exploring Client Preferences for Psychological Counselors in a Chinese Online Health Community: Longitudinal Study

BACKGROUND

Although online health communities are acknowledged for their role in bridging the supply-demand gap in mental health services, the client decision-making process in these environments remains underexplored.

OBJECTIVE

This study aimed to explore the impact of different signals presented on psychological counselors' home pages on clients' choices.

METHODS

Adopting signaling theory as the framework, this study classified information into online and offline signals and developed a theoretical model to examine client choice behaviors. We collected data from 487 psychological counselors in a leading Chinese online mental health community during March, June, September, and December 2023. Based on these data, we constructed a 4-period balanced panel dataset. A fixed effects model was used to analyze which signals influence clients' choices of psychological counselors.

RESULTS

Regarding online signals, the service price (β=0.186, P<.001) and online reputation (β=0.489, P=.002) of psychological counselors positively influence clients' choices. Concerning offline signals, psychological counselors' practical experience (β=0.007, P<.001) is positively related to clients' choices. Moreover, the results indicate that the relationship between a counselor's prosocial behavior and clients' choices is not linear but rather exhibits an inverted U-shape.

CONCLUSIONS

This study reveals that the varied information provided by psychological counselors has distinct impacts on clients' choices in online health communities. It broadens the application of signaling theory to online behaviors and emphasizes the importance of both online and offline signals. These insights offer strategic guidance for counselors and online platforms to better meet potential clients' needs by optimizing the information presented on psychological counselors' home pages.

Who Benefits from Acute Psychiatric Home Treatment? A Systematic Review

Home treatment (HT) treats patients in an acute crisis through an interdisciplinary team with daily appointments for a short treatment period. The effectiveness of HT has already been confirmed. However, only few studies addressed specific patient characteristics associated outcome of treatment. This study aimed to identify patient characteristics associated with successful outcomes of HT. A systematic literature search was conducted according to the PRISMA guidelines. A total of 13 studies were included in the systematic review. Being employed, having a regular income, having an anxiety disorder and family involvement were associated with a successful treatment outcome in HT. High symptom severity and former hospital admissions were associated with unsuccessful treatment outcome in HT in the selected studies. HT seems to be especially beneficial for patients with paid employment or regular income, patients with anxiety disorders, and patients with familial or other social support.

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

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

Prediction of adolescent psychotherapy outcomes using youth- and caregiver-reported symptoms data

OBJECTIVE

We used longitudinal youth- and caregiver-reports of adolescent psychological symptoms from three samples of youth receiving mental health services in routine treatment settings to derive expected change trajectories and identify cases at risk for treatment failure.

METHOD

Participants were 1906 youth (1053 caregivers) receiving treatment in community mental health settings, merged across three samples. The Symptoms and Functioning Severity Scale (SFSS) was used as an indicator of weekly clinical change. Multilevel modeling methods were used to develop expected change trajectories and identify cases at risk for treatment failure (not on track; NOT). Logistic regression was used to predict client improvement as a function of NOT status.

RESULTS

The SFSS was a reliable indicator of therapeutic change according to youth-reported symptoms. Caregiver reports were not as robust. Whereas predictive accuracy of NOT status yielded moderately high sensitivity in detecting improvement according to youth report, caregiver reports were not as predictive.

CONCLUSIONS

The youth-reported version of the SFSS-based algorithm seems appropriate for implementation in clinical care. Future studies should search for similarly predictive measures for caregivers.

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol

Introduction

Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy most benefits the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs.

Methods

This study is a double-blinded randomized controlled trial recruiting individuals experiencing depression. The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm, or (2) an assessment made by a group of healthcare professionals. Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-min phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources.

Discussion

Artificial intelligence and providing patients with varying intensities of care can increase the efficiency of mental health care services. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to online psychotherapy by allocating the correct intensity of therapist care for individuals diagnosed with depression. This will be done by comparing a decision-making machine learning algorithm to a multi-professional care team. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources with the convergence of technologies and healthcare.

Ethics

The study received ethics approval and began participant recruitment in December 2022. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2024.

Clinical trial registration

ClinicalTrials.Gov, identifier NCT04747873.

Participants' Engagement With and Results From a Web-Based Integrative Population Mental Wellness Program (CHAMindWell) During the COVID-19 Pandemic: Program Evaluation Study

BACKGROUND

The COVID-19 pandemic involved a prolonged period of collective trauma and stress during which substantial increases in mental health concerns, like depression and anxiety, were observed across the population. In this context, CHAMindWell was developed as a web-based intervention to improve resilience and reduce symptom severity among a public health care system's patient population.

OBJECTIVE

This program evaluation was conducted to explore participants' engagement with and outcomes from CHAMindWell by retrospectively examining demographic information and mental health symptom severity scores throughout program participation.

METHODS

We examined participants' symptom severity scores from repeated, web-based symptom screenings through Computerized Adaptive Testing for Mental Health (CAT-MH) surveys, and categorized participants into symptom severity-based tiers (tier 1=asymptomatic to mild; tier 2=moderate; and tier 3=severe). Participants were provided tier-based mindfulness resources, treatment recommendations, and referrals. Logistic regressions were conducted to evaluate associations between demographic variables and survey completion. The McNemar exact test and paired sample t tests were performed to evaluate changes in the numbers of participants in tier 1 versus tier 2 or 3 and changes in depression, anxiety, and posttraumatic stress disorder severity scores between baseline and follow-up.

RESULTS

The program enrolled 903 participants (664/903, 73.5% female; 556/903, 61.6% White; 113/903, 12.5% Black; 84/903, 9.3% Asian; 7/903, 0.8% Native; 36/903, 4% other; and 227/903, 25.1% Hispanic) between December 16, 2020, and March 17, 2022. Of those, 623 (69%) completed a baseline CAT-MH survey, and 196 completed at least one follow-up survey 3 to 6 months after baseline. White racial identity was associated with completing baseline CAT-MH (odds ratio [OR] 1.80, 95% CI 1.14-2.84; P=.01). Participants' odds of having symptom severity below the clinical threshold (ie, tier 1) were significantly greater at follow-up (OR 2.60, 95% CI 1.40-5.08; P=.001), and significant reductions were observed across symptom domains over time.

CONCLUSIONS

CHAMindWell is associated with reduced severity of mental health symptoms. Future work should aim to address program engagement inequities and attrition and compare the impacts of CHAMindWell to a control condition to better characterize its effects.

Trajectories of depression symptoms, anxiety symptoms and functional impairment during internet-enabled cognitive-behavioural therapy

Underlying classes capture differences between patient symptom trajectories during psychological therapy. This has not been explored for one-to-one internet-delivered therapy or functional impairment trajectories. Patients experiencing depression or anxiety received cognitive-behavioural therapy with a therapist using an online chat platform (N = 52,029). Trajectory classes of depression symptoms (PHQ9), anxiety symptoms (GAD7) and functional impairment (WSAS) were investigated using growth mixture modelling. Multinomial regressions tested associations between baseline variables and trajectory class. A four-class trajectory model was selected for each outcome, and these were highly similar. Each outcome showed three classes with initially moderate-severe symptoms or impairment: one demonstrated no change, one gradual improvement and one fast improvement. A fourth class had mild baseline scores and minimal improvement. In the moderate-severe classes, patients in the two with improvement were more likely to be employed and not to have obsessive-compulsive disorder. Fast improvement was likelier than gradual improvement or no change for patients with older age, no disability (e.g., physical, learning), or lower comorbid symptom or impairment scores. Associations with functional impairment classes were more similar to associations with depression classes than anxiety classes. Results were largely consistent with findings from face-to-face therapy. This study is an important step towards personalising therapy in terms of suitability and continuation.

Trajectories of depression and anxiety symptom severity during psychological therapy for common mental health problems

BACKGROUND

There is substantial variation in patient symptoms following psychological therapy for depression and anxiety. However, reliance on endpoint outcomes ignores additional interindividual variation during therapy. Knowing a patient's likely symptom trajectories could guide clinical decisions. We aimed to identify latent classes of patients with similar symptom trajectories over the course of psychological therapy and explore associations between baseline variables and trajectory class.

METHODS

Patients received high-intensity psychological treatment for common mental health problems at National Health Service Improving Access to Psychological Therapies services in South London (N = 16 258). To identify trajectories, we performed growth mixture modelling of depression and anxiety symptoms over 11 sessions. We then ran multinomial regressions to identify baseline variables associated with trajectory class membership.

RESULTS

Trajectories of depression and anxiety symptoms were highly similar and best modelled by four classes. Three classes started with moderate-severe symptoms and showed (1) no change, (2) gradual improvement, and (3) fast improvement. A final class (4) showed initially mild symptoms and minimal improvement. Within the moderate-severe baseline symptom classes, patients in the two showing improvement as opposed to no change tended not to be prescribed psychotropic medication or report a disability and were in employment. Patients showing fast improvement additionally reported lower baseline functional impairment on average.

CONCLUSIONS

Multiple trajectory classes of depression and anxiety symptoms were associated with baseline characteristics. Identifying the most likely trajectory for a patient at the start of treatment could inform decisions about the suitability and continuation of therapy, ultimately improving patient outcomes.

The association between trajectories of change in social functioning and psychological treatment outcome in university students: a growth mixture model analysis

BACKGROUND

The transition to university and resultant social support network disruption can be detrimental to the mental health of university students. As the need for mental health support is becoming increasingly prevalent in students, identification of factors associated with poorer outcomes is a priority. Changes in social functioning have a bi-directional relationship with mental health, however it is not clear how such measures may be related to effectiveness of psychological treatments.

METHODS

Growth mixture models were estimated on a sample of 5221 students treated in routine mental health services to identify different trajectories of change in self-rated impairment in social leisure activities and close relationships during the course of treatment. Multinomial regression explored associations between trajectory classes and treatment outcomes.

RESULTS

Five trajectory classes were identified for social leisure activity impairment while three classes were identified for close relationship impairment. In both measures most students remained mildly impaired. Other trajectories included severe impairment with limited improvement, severe impairment with delayed improvement, and, in social leisure activities only, rapid improvement, and deterioration. Trajectories of improvement were associated with positive treatment outcomes while trajectories of worsening or stable severe impairment were associated with negative treatment outcomes.

CONCLUSIONS

Changes in social functioning impairment are associated with psychological treatment outcomes in students, suggesting that these changes may be associated with treatment effectiveness as well as recovery experiences. Future research should seek to establish whether a causal link exists to understand whether integrating social support within psychological treatment may bring additional benefit for students.

Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study

BACKGROUND

As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.

OBJECTIVE

The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.

METHODS

We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.

RESULTS

Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings.

CONCLUSIONS

This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.

A randomized controlled trial on using predictive algorithm to adapt level of psychological care for community college students: STAND triaging and adapting to level of care study protocol

BACKGROUND

There is growing interest in using personalized mental health care to treat disorders like depression and anxiety to improve treatment engagement and efficacy. This randomized controlled trial will compare a traditional symptom severity decision-making algorithm to a novel multivariate decision-making algorithm for triage to and adaptation of mental health care. The stratified levels of care include a self-guided online wellness program, coach-guided online cognitive behavioral therapy, and clinician-delivered psychotherapy with or without pharmacotherapy. The novel multivariate algorithm will be comprised of baseline (for triage and adaptation) and time-varying variables (for adaptation) in four areas: social determinants of mental health, early adversity and life stressors, predisposing, enabling, and need influences on health service use, and comprehensive mental health status. The overarching goal is to evaluate whether the multivariate algorithm improves adherence to treatment, symptoms, and functioning above and beyond the symptom-based algorithm.

METHODS/DESIGN

This trial will recruit a total of 1000 participants over the course of 5 years in the greater Los Angeles Metropolitan Area. Participants will be recruited from a highly diverse sample of community college students. For the symptom severity approach, initial triaging to level of care will be based on symptom severity, whereas for the multivariate approach, the triaging will be based on a comprehensive set of baseline measures. After the initial triaging, level of care will be adapted throughout the duration of the treatment, utilizing either symptom severity or multivariate statistical approaches. Participants will complete computerized assessments and self-report questionnaires at baseline and up to 40 weeks. The multivariate decision-making algorithm will be updated annually to improve predictive outcomes.

DISCUSSION

Results will provide a comparison on the traditional symptom severity decision-making and the novel multivariate decision-making with respect to treatment adherence, symptom improvement, and functional recovery. Moreover, the developed multivariate decision-making algorithms may be used as a template in other community college settings. Ultimately, findings will inform the practice of level of care triage and adaptation in psychological treatments, as well as the use of personalized mental health care broadly.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05591937, submitted August 2022, published October 2022.

Raising the self-esteem and reducing irrational beliefs of schoolchildren: The moderating and main effect study

BACKGROUND

Several systematic reviews and meta-analyses studies have called for moderators of treatment outcomes and their main effect with regard to disadvantaged populations. In view of that, this study investigated the impacts and moderators of rational emotive behavior therapy (REBT) on the self-esteem and irrational beliefs of Schoolchildren in Ebonyi State Nigeria.

METHODS

A group randomized controlled design was utilized to assign 55 schoolchildren to the treatment group and 55 schoolchildren to waitlisted control group. Two self-report measures (Self-Esteem Scale and Children Adolescent Scale of Irrationality) were used to assess the participants. There were pretest, posttest, and follow-up tests given at different intervals to ascertain the baseline, main effect, and long-term effects of the treatment. The data collected were analyzed using a 2-way analysis of covariance statistic.

RESULTS

The results of the 2-way analysis of covariance demonstrated a difference between participants in the waitlisted control group at the pretest, posttest, and follow-up test and a positive improvement in schoolchildren with illogical beliefs as a result of exposure to REBT intervention. It was discovered that the REBT intervention changed schoolchildren's self-esteem and irrational views into rational ones. A later test result supported the intervention's consistent and significant effects in lowering illogical beliefs and raising students' self-esteem. The results also showed that there is no connection between gender and group membership.

CONCLUSION

This study suggests that REBT is a significant treatment strategy that reduces irrational beliefs and improves the self-esteem of primary school children. Based on these outcomes, further studies should replicate the study in other cultures with such a disadvantaged group.

Trajectories of depression and generalised anxiety symptoms over the course of cognitive behaviour therapy in primary care: an observational, retrospective cohort

BACKGROUND

Cognitive-behavioural therapy (CBT) has been shown to be an effective treatment for depression and anxiety. However, most research has focused on the sum scores of symptoms. Relatively little is known about how individual symptoms respond.

METHODS

Longitudinal models were used to explore how depression and generalised anxiety symptoms behave over the course of CBT in a retrospective, observational cohort of patients from primary care settings (n = 5306). Logistic mixed models were used to examine the probability of being symptom-free across CBT appointments, using the 9-item Patient Health Questionnaire and the 7-item Generalised Anxiety Disorder scale as measures.

RESULTS

All symptoms improve across CBT treatment. The results suggest that low mood/hopelessness and guilt/worthlessness improved quickest relative to other depressive symptoms, with sleeping problems, appetite changes, and psychomotor retardation/agitation improving relatively slower. Uncontrollable worry and too much worry were the anxiety symptoms that improved fastest; irritability and restlessness improved the slowest.

CONCLUSIONS

This research suggests there is a benefit to examining symptoms rather than sum scores alone. Investigations of symptoms provide the potential for precision psychiatry and may explain some of the heterogeneity observed in clinical outcomes when only sum scores are considered.

The difference between shorter- versus longer-term psychotherapy for adult mental health disorders: a systematic review with meta-analysis

BACKGROUND

The optimal psychotherapy duration for mental health disorders is unclear. Our aim was to assess the beneficial and harmful effects of shorter- versus longer-term psychotherapy for adult mental health disorders.

METHOD

We searched relevant databases and websites for published and unpublished randomised clinical trials assessing different durations of the same psychotherapy type before June 27, 2022. Our methodology was based on Cochrane and an eight-step procedure. Primary outcomes were quality of life, serious adverse events, and symptom severity. Secondary outcomes were suicide or suicide-attempts, self-harm, and level of functioning.

RESULTS

We included 19 trials randomising 3,447 participants. All trials were at high risk of bias. Three single trials met the required information size needed to confirm or reject realistic intervention effects. One single trial showed no evidence of a difference between 6 versus 12 months dialectical behavioral therapy for borderline personality when assessing quality of life, symptom severity, and level of functioning. One single trial showed evidence of a beneficial effect of adding booster sessions to 8 and 12 weeks of internet-based cognitive behavioral therapy for depression and anxiety when assessing symptom severity and level of functioning. One single trial showed no evidence of a difference between 20 weeks versus 3 years of psychodynamic psychotherapy for mood- or anxiety disorders when assessing symptom severity and level of functioning. It was only possible to conduct two pre-planned meta-analyses. Meta-analysis showed no evidence of a difference between shorter- and longer-term cognitive behavioural therapy for anxiety disorders on anxiety symptoms at end of treatment (SMD: 0.08; 95% CI: -0.47 to 0.63; p = 0.77; I2 = 73%; four trials; very low certainty). Meta-analysis showed no evidence of a difference between shorter and longer-term psychodynamic psychotherapy for mood- and anxiety disorders on level of functioning (SMD 0.16; 95% CI -0.08 to 0.40; p = 0.20; I2 = 21%; two trials; very low certainty).

CONCLUSIONS

The evidence for shorter versus longer-term psychotherapy for adult mental health disorders is currently unclear. We only identified 19 randomised clinical trials. More trials at low risk of bias and at low risk of random errors assessing participants at different levels of psychopathological severity are urgently needed.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019128535.

Understanding the psychological therapy treatment outcomes for young adults who are not in education, employment, or training (NEET), moderators of outcomes, and what might be done to improve them

BACKGROUND

To determine: whether young adults (aged 18-24) not in education, employment or training (NEET) have different psychological treatment outcomes to other young adults; any socio-demographic or treatment-related moderators of differential outcomes; and whether service-level changes are associated with better outcomes for those who are NEET.

METHODS

A cohort was formed of 20 293 young adults treated with psychological therapies in eight Improving Access to Psychological Therapies services. Pre-treatment characteristics, outcomes, and moderators of differential outcomes were compared for those who were and were not NEET. Associations between outcomes and the following were assessed for those that were NEET: missing fewer sessions, attending more sessions, having a recorded diagnosis, and waiting fewer days between referral and starting treatment.

RESULTS

Those who were NEET had worse outcomes: odds ratio (OR) [95% confidence interval (CI)] for reliable recovery = 0.68 (0.63-0.74), for deterioration = 1.41 (1.25-1.60), and for attrition = 1.31 (1.19-1.43). Ethnic minority participants that were NEET had better outcomes than those that were White and NEET. Living in deprived areas was associated with worse outcomes. The intensity of treatment (high or low) did not moderate outcomes, but having more sessions was associated with improved outcomes for those that were NEET: odds (per one-session increase) of reliable recovery = 1.10 (1.08-1.12), deterioration = 0.94 (0.91-0.98), and attrition = 0.68 (0.66-0.71).

CONCLUSIONS

Earlier treatment, supporting those that are NEET to attend sessions, and in particular, offering them more sessions before ending treatment might be effective in improving clinical outcomes. Additional support when working with White young adults that are NEET and those in more deprived areas may also be important.

Patient-reported outcome measures in depression

AIM

Patient-reported outcome measures (PROMs) are increasingly important as a mean for quality assurance. Feasible estimates of recovery can be achieved through the application of Jacobson plots, which is a simple demonstration of the outcome of each case, recommended for clinical use. We applied this approach with PROMs collected regarding group psychotherapy in a mental health service (MHS) setting. We hypothesized a recovery rate of above 50% of all cases, expecting a lower recovery rate amongst patients with severe depression.

METHODS

We made a secondary sub-sample analysis of data from patients with unipolar depression (N = 171) within a pragmatic, non-inferiority, randomized controlled clinical trial comparing two cognitive behavior therapy (CBT) group interventions. The treatment consisted of 14 2-hours weekly group CBT sessions. We collected depression PROMs with the Becks Depression Inventory-II and functional levels PROMs with the Work and Social Adjustment Scale at baseline, end-of-treatment, and at a 6-months follow-up.

RESULTS

At follow-up, 35% (N = 43/123) of cases with moderate or severe depression (BDI > 19) at baseline reached scores below the cut-off for moderate depression. Recovery rates in severe cases were significantly lower (26.5 vs 52.5%; p = 0.0004). We observed severe functional impairment in 36% of the patients at baseline (52/144) and observed no changes or worsening in scores at a 6-months follow-up in 44% of the patients (64/144).

CONCLUSION

We achieved satisfactory remission rates for patients with moderate depression. Patients with severe depression and patients with functional impairment reached recovery rates below the standard of comparable MHSs. Improved MHSs for these patients are needed.

Moderators of treatment effect of Prompt Mental Health Care compared to treatment as usual: Results from a randomized controlled trial

BACKGROUND

In this exploratory study, we investigated a comprehensive set of potential moderators of response to the primary care service Prompt Mental Health Care (PMHC).

METHODS

Data from an RCT of PMHC (n = 463) versus treatment as usual (TAU, n = 215) were used. At baseline mean age was 34.8, 66.7% were women, and 91% scored above caseness for depression (PHQ-9) and 87% for anxiety (GAD-7).

OUTCOMES

change in symptoms of depression and anxiety and change in remission status from baseline to six- and 12- months follow-up. Potential moderators: sociodemographic, lifestyle, social, and cognitive variables, variables related to (mental) health problem and care. Each moderator was examined in generalized linear mixed models with robust maximum likelihood estimation.

RESULTS

Effect modification was only identified for anxiolytic medication for change in symptoms of depression and anxiety; clients using anxiolytic medication showed less effect of PMHC relative to TAU (all p < 0.001), although this result should be interpreted with caution due to the low number of anxiolytic users in the sample. For remission status, none of the included variables moderated the effect of treatment.

CONCLUSION

As a treatment for depression and/or anxiety, PMHC mostly seems to work equally well as compared to TAU across a comprehensive set of potential moderators.

Usage Patterns of Telepsychology and Face-to-Face Psychotherapy: Clients' Profiles and Perceptions

Background

Currently, most people who might need mental health care services do not receive them due to a number of reasons. Many of these reasons can be overcome by telepsychology, in other words, the use of ICT technologies for therapy (e.g., phone, videoconferencing, and apps); given that it facilitates access to specialized interventions. In fact, telepsychology is currently offered as an active service in many psychotherapy centers. However, its usage, how it is perceived, and who uses it are still largely unknown.

Objective

The aim of this study was (1) to determine if any pattern exists in the usage of telepsychology and face-to-face psychology, (2) to clarify people's perception of telepsychology in terms of the advantages, barriers and efficacy of online psychotherapy, and (3) to examine usage patterns in terms of individual characteristics and identify patients' profiles.

Methods

An online survey was conducted on a convenience sample of 514 subjects recluted by using an online advertisement. The inclusion criteria were: (1) to be older than 18 years old and (2) to answer completely the questionnaire. Cluster analysis, ANOVAs, and discriminant analysis were performed to test our research objectives.

Results

Three usage clusters were found: (1) face-to-face psychotherapy (57%; n = 292); (2) non-therapy (36.8%; n = 189); and (3) combined face-to-face psychotherapy and telepsychology (6.4%; n = 33). In addition, the perception of telepsychology varied among usage clusters, but a common perception emerged about the main telepsychology advantages, barriers and efficacy. Finally, the results showed that personal characteristics differentiated people in each of these clusters.

Conclusion

The most common form of access to psychotherapy is the face-to-face form but the second way of delivery was a combination between face to face and online psychotherapy (research objective 1). People who combine face to face with online psychotherapy perceives this last as more efficient and with less barriers to access (research objective 2). Finally, some characteristic as eHealth experience and sociodemographic variables can help to identify people that will attend telepsychology initiatives (research objective 3). These clusters provide insight into opportunities for face-to-face and online patient engagement strategies.

Predictors of Disengagement and Symptom Improvement Among Adults With Depression Enrolled in Talkspace, a Technology-Mediated Psychotherapy Platform: Naturalistic Observational Study

BACKGROUND

Depression is a common psychiatric condition with an estimated lifetime prevalence for major depression of 16.6% in the US adult population and is effectively treated through psychotherapy. The widespread availability of the internet and personal devices such as smartphones are changing the landscape of delivery of psychotherapy; however, little is known about whether and for whom this type of therapy is beneficial, and whether having synchronous video-based sessions provides additional benefits to clients above and beyond messaging-based therapy.

OBJECTIVE

This study examined the outcomes associated with the use of a digital platform (Talkspace) for technology-mediated psychotherapy. We examined the duration of client engagement in therapy and client depression score trajectories over 16 weeks. We explored the association of client characteristics, therapist characteristics, and service plan type with time-to-disengagement and trajectories of change in depression scores.

METHODS

This naturalistic observational study assessed data collected routinely by the platform between January 2016 and January 2018 and examined psychotherapy outcomes among a large representative sample of adult clients with clinically significant depression. Treatment disengagement was defined as a lack of client-initiated communication for more than 4 weeks. Clients completed the Patient Health Questionnaire-8 item (PHQ-8) at intake and every 3 weeks via an in-app survey. Cox regression analysis was used to examine the time until and predictors of disengagement. Changes in depression scores and predictors of change over time were examined using mixed-effects regression.

RESULTS

The study included 5890 clients and 1271 therapists. Client scores on the PHQ-8 declined over time, with the average client improving from a score of 15 to below the clinical cutoff of 10 by week 6. At the same time point, 37% of clients had disengaged from the therapy. When combined into a final Cox regression model, those who were more likely to disengage were clients aged 18 to 25 years versus those aged ≥50 years (odds ratio [OR] 0.82, 95% CI 0.74-0.9; P<.001), had higher education (OR 1.14, 95% CI 1.06-1.22; P<.001), had been in therapy before (OR 1.09, 95% CI 1.02-1.17; P=.01), and were living with a partner but unmarried versus single (OR 1.14, 95% CI 1.02-1.27; P=.02). Having a therapist with >10 years of experience was related to lower odds of disengagement (OR 0.87, 95% CI 0.8-0.94; P=.01). When combined into a final regression model predicting improvement in depression scores over time, clients showing more improvement were those with an associate's degree or higher (linear estimate=-0.07, P=.002) and higher intake PHQ-8 scores (estimate=3.73, P<.001). There were no differences based on the plan type.

CONCLUSIONS

Our findings add to the growing literature showing the benefits of technology-mediated psychotherapy over a relatively brief period (16 weeks).

Mental health outcomes in patients with a long-term condition: analysis of an Improving Access to Psychological Therapies service

BACKGROUND

Having a long-term condition (LTC) significantly affects mental health. UK policy requires effective mental health provisions for patients with an LTC, generally provided by Improving Access to Psychological Therapies (IAPT) services. National IAPT data suggest that patients with an LTC typically demonstrate poorer outcomes compared with patients without an LTC. However, exploration of confounding factors and different outcome variables is limited.

AIMS

To establish the association of LTC status with demographic and clinical factors, and clinical mental health outcomes.

METHOD

Anonymised patient-level data from a London IAPT service during January 2019 to October 2020 were used in this cohort study, to compare differences between LTC and non-LTC groups on sociodemographic and clinical variables. Binary logistic and multiple linear regression models were constructed for binary outcome variables (recovery and reliable improvement) and continuous outcomes (distress and functioning), respectively.

RESULTS

Patients with an LTC were more likely to be female; older; from a Black, mixed or other ethnic background; and have greater social deprivation. Across the four clinical outcomes (recovery, reliable improvement, final psychological distress and final functioning), having an LTC significantly predicted poorer outcomes even after controlling for sociodemographic and clinical baseline variables. For three outcome variables, greater social deprivation and being discharged during the COVID-19 pandemic also predicted poorer clinical outcomes.

CONCLUSIONS

LTC status has a negative effect on mental health outcomes in IAPT services, independent of associated variables such as severity of baseline mental health symptoms, ethnicity and social deprivation. Effective psychological treatment for patients with an LTC remains an unresolved priority.

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.

Predicting cumulative effect of lifestyle risk factors for complex disease

In medical domain, risk factors are often used to model disease predictions. In order to make the most use of the predictive models, linking the model with real patient data generates personalized disease progression and predictions. However, the risk factors are fragmented all over medical literature, certain risks can be accumulated for a disease and the aggregated probability may increase or decrease the occurrence of a disease. In this paper, a risk predictive framework which forms a base for a complete risk prediction model that can be used for various health applications is proposed.

Are depressed patients' coping strategies associated with psychotherapy treatment outcomes?

BACKGROUND

In theory, depression is thought to be associated with deficits in adaptive and excesses in maladaptive coping strategies. This study aimed to investigate associations between coping strategies and depression treatment outcomes.

METHOD

Participants (N = 126) completed measures of adaptive and maladaptive coping strategies before and after accessing evidence-based psychotherapies for depression. The primary outcome was self-reported depression severity measured with the Patient Health Questionnaire (PHQ-9). Hierarchical regression was used to investigate associations between coping strategies and post-treatment depression symptoms, controlling for therapeutic alliance and relevant demographics.

RESULTS

Lower pre-treatment engagement coping and higher rumination predicted higher post-treatment depression, but both of these effects became non-significant after controlling for baseline depression severity. Similarly, correlations between change in rumination and change in depression were no longer significant after controlling for baseline severity.

CONCLUSIONS

Deficits in adaptive (engagement) and excesses in maladaptive (rumination) coping strategies may simply be proxy indicators (epiphenomena) of depression severity.

PRACTITIONER POINTS

Lower pre-treatment engagement coping predicted higher post-treatment depression Higher pre-treatment rumination predicted higher post-treatment depression Change in rumination during treatment correlated with change in depression symptoms However, none of the above associations remained statistically significant after controlling for baseline depression severity.

Stratified Care vs Stepped Care for Depression: A Cluster Randomized Clinical Trial

IMPORTANCE

Depression is a major cause of disability worldwide. Although empirically supported treatments are available, there is scarce evidence on how to effectively personalize psychological treatment selection.

OBJECTIVE

To compare the clinical effectiveness and cost-effectiveness of 2 treatment selection strategies: stepped care and stratified care.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, cluster randomized clinical trial recruited participants from the English National Health Service from July 5, 2018, to February 1, 2019. Thirty clinicians working across 4 psychological therapy services were randomly assigned to provide stratified (n = 15) or stepped (n = 15) care. In stepped care, patients sequentially access low-intensity guided self-help followed by high-intensity psychotherapy. In stratified care, patients are matched with either low- or high-intensity treatments at initial assessment. Data were analyzed from May 18, 2020, to October 13, 2021, using intention-to-treat principles.

INTERVENTIONS

All clinicians used the same interview schedule to conduct initial assessments with patients seeking psychological treatment for common mental disorders, but those in the stratified care group received a personalized treatment recommendation for each patient generated by a machine learning algorithm. Eligible patients received either stratified or stepped care (ie, treatment as usual).

MAIN OUTCOMES AND MEASURES

The preregistered outcome was posttreatment reliable and clinically significant improvement (RCSI) of depression symptoms (measured using the 9-item Patient Health Questionnaire). The RCSI outcome was compared between groups using logistic regression adjusted for baseline severity. Cost-effectiveness analyses compared incremental costs and health outcomes of the 2 treatment pathways.

RESULTS

A total of 951 patients were included (618 women among 950 with data available [65.1%]; mean [SD] age, 38.27 [14.53] years). The proportion of cases of RCSI was significantly higher in the stratified care arm compared with the stepped care arm (264 of 505 [52.3%] vs 134 of 297 [45.1%]; odds ratio, 1.40 [95% CI, 1.04-1.87]; P = .03). Stratified care was associated with a higher mean additional cost per patient (£104.5 [95% CI, £67.5-£141.6] [$139.83 (95% CI, $90.32-$189.48)]; P < .001) because more patients accessed high-intensity treatments (332 of 583 [56.9%] vs 107 of 368 [29.1%]; χ2 = 70.51; P < .001), but this additional cost resulted in an approximately 7% increase in the probability of RCSI.

CONCLUSIONS AND RELEVANCE

In this cluster randomized clinical trial of adults with common mental disorders, stratified care was efficacious and cost-effective for the treatment of depression symptoms compared with stepped care. Stratified care can improve depression treatment outcomes at a modest additional cost.

TRIAL REGISTRATION

isrctn.org Identifier: ISRCTN11106183.

Patient experience of lasting negative effects of psychological interventions for anxiety and depression in secondary mental health care services: a national cross-sectional study

BACKGROUND

Patients who undergo psychological treatment can report both negative and positive effects, but evidence of factors influencing the likelihood of negative effects is limited.

AIMS

To identify aspects of the organisation and delivery of secondary care psychological treatment services that are associated with patient experiences of negative effects.

METHOD

Cross-sectional survey of people with anxiety and depression who ended psychological treatment delivered by 50 NHS trusts in England. Respondents were asked about how their treatment was organised and delivered and whether they experienced lasting negative effects.

RESULTS

Of 662 respondents, 90 (14.1%) reported experiencing lasting negative effects. People over the age of 65 were less likely than younger respondents to report negative effects. There was an association between reporting neutral or negative effects and not being referred at what respondents considered to be the right time (OR = 1.712, 95% CI = 1.078-2.726), not receiving the right number of sessions (OR = 3.105, 95% CI = 1.934-4.987), and not discussing progress with their therapist (OR 2.063, 95% CI = 1.290-3.301).

CONCLUSIONS

One in seven patients who took part in this survey reported lasting negative effects from psychological treatment. Steps should be taken to prepare people for the potential for negative experiences of treatment, and progress reviewed during therapy in an effort to identify and prevent negative effects.

Is alcohol use associated with psychological treatment attendance and clinical outcomes?

OBJECTIVES

To investigate associations between alcohol use, psychological treatment attendance, and clinical outcomes.

METHODS

We analysed electronic health records for N = 7,986 patients accessing psychological treatment for common mental disorders. Data were collected for pre-treatment alcohol use (average units per week) and severity of dependence (SDS), number of therapy contacts attended, pre- and post-treatment anxiety (GAD-7), and depression (PHQ-9) symptom severity. Hierarchical regression was used to examine associations between alcohol use/dependence and post-treatment symptom severity controlling for intake severity and relevant confounders.

RESULTS

After controlling for confounders, alcohol use had significant nonlinear associations with pre-treatment depression severity (R²  = .54, p < .01, cubic trend), and post-treatment anxiety (R²  = .23, p < .01, quadratic trend). Alcohol use was not significantly associated with intake anxiety, post-treatment depression or treatment duration. SDS was not significantly associated with depression severity, alcohol severity, or total contacts after controlling for confounders.

CONCLUSION

Alcohol users are just as likely to engage in and benefit from evidence-based psychological treatments for depression in primary care. A nonlinear association between alcohol use and anxiety treatment outcomes indicates that light-to-moderate drinkers have some shared characteristic that favours treatment response.

PRACTITIONER POINTS

Depression and anxiety problems often co-occur with alcohol use and dependence. Conventional wisdom in the field suggests that heavy alcohol users may not engage well or benefit from psychological therapies for depression and anxiety. We found no empirical support for the above assumptions in a large clinical sample. Alcohol consumption was not significantly associated with treatment attendance or depression treatment outcomes. A nonlinear association between alcohol use and anxiety outcomes suggests that moderate drinkers may have some shared characteristic that favours treatment response.

Center Effects: Counseling Center Variables as Predictors of Psychotherapy Outcomes

Treatment context may have a unique impact on psychotherapy outcomes, above and beyond client, therapist, and therapy process variables. University counseling centers represent one such treatment context facing increasing treatment demands. This study examined the role of counseling centers and center variables in explaining differences in psychotherapy outcomes. The Center for Collegiate Mental Health, a large practice–research network, contained data from 116 counseling centers, 2,362 therapists, and 58,423 clients. Multilevel modeling tested if some counseling centers systematically achieved better outcomes than others (a “center effect”). Outcome was operationalized as clients’ magnitude and rate of change in distress across treatment. Results showed a relatively small “center effect” for both outcomes. Analyses sought to explain that center effect through administrative policies and characteristics. As a group, these variables partially explained the center effect. None explained a large portion of total outcome variance. Potential future implications for policy and advocacy efforts are discussed.

The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis

BACKGROUND

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

METHODS

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

RESULTS

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3-4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3-4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

CONCLUSIONS

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

A personalized index to inform selection of a trauma-focused or non-trauma-focused treatment for PTSD

PTSD treatment guidelines recommend several treatments with extensive empirical support, including Prolonged Exposure (PE), a trauma-focused treatment and Present-Centered Therapy (PCT), a non-trauma-focused therapy. Research to inform treatment selection has yielded inconsistent findings with single prognostic variables that are difficult to integrate into clinical decision-making. We examined whether a combination of prognostic factors can predict different benefits in a trauma-focused vs. a non-trauma-focused psychotherapy. We applied a multi-method variable selection procedure and developed a prognostic index (PI) with a sample of 267 female veterans and active-duty service members (mean age 45; SD = 9.37; 53% White) with current PTSD who began treatment in a randomized clinical trial comparing PE and PCT. We conducted linear regressions predicting outcomes (Clinician-Administered PTSD Scale score) with treatment condition, the PI, and the interaction between the PI and treatment condition. The interaction between treatment type and PI moderated treatment response, moderated post-treatment symptom severity, b = 0.30, SE_b = 0.15 [95% CI: 0.01, 0.60], p = .049. For the 64% of participants with the best prognoses, PE resulted in better post-treatment outcomes; for the remainder, there was no difference. Use of a PI may lead to optimized patient outcomes and greater confidence when selecting trauma-focused treatments.

How much therapy is enough? The dose-response effect and its moderators in a psychology training clinic

OBJECTIVE

The current study aims to sharpen the understanding of the psychotherapy dose-response effect and its moderators in a psychology training clinic.

METHOD

Data were extracted from 58 client records. Weekly Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, as well as Outcomes Questionnaire-45.2, administered every fifth session, assessed whether clients achieved reliable change (RC) and clinically significant and reliable change (CSR) during treatment. Survival analyses were conducted to determine the sessions required for 50% of the sample to achieve these outcomes. Multilevel Cox frailty regressions were used to investigate client-and-therapy-based moderators.

RESULTS

The median time for 50% of the sample to achieve RC was 8-10 sessions and 11 sessions to achieve initial CSR. Past treatment history was a significant moderator of time to achieve RC.

CONCLUSIONS

From a population perspective, psychotherapy is most beneficial to patients early in treatment. Sharper understanding of the number of sessions required to achieve meaningful change can inform practice in training settings.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

Dose-response patterns in low and high intensity cognitive behavioral therapy for common mental health problems

BACKGROUND

Cognitive-behavioral therapy (CBT) is effective for the treatment of common mental health problems, but the number of sessions required to maximize improvement in routine care remains unclear.

AIM

This study aimed to examine the dose-response effect in low (LiCBT) and high (HiCBT) intensity CBT delivered in stepped care services.

METHODS

A multi-service data set included N = 102 206 patients across N = 16 services. The study included patients with case-level depression and/or anxiety symptoms who accessed LiCBT and/or HiCBT. Patients with posttreatment reliable and clinically significant improvement in standardized outcome measures (PHQ-9, GAD-7) were classified as treatment responders. Survival analyses assessed the number of sessions necessary to detect 50%, 75%, and 95% of treatment responders. The 50% and 95% percentiles were used to define the lower and upper boundaries of an adequate dose of therapy that could be used to inform the timing of treatment progress reviews. Analyses were then stratified by diagnosis, and cox regression was used to identify predictors of time-to-remission.

RESULTS

Most responders (95%) attained RCSI within 7 sessions of LiCBT and 14 sessions of HiCBT. Patients with social anxiety disorder, posttraumatic stress disorder, and obsessive-compulsive disorder required HiCBT and lengthier treatments (6-16 sessions) to maximize improvement.

CONCLUSIONS

Distinctive dose-response patterns are evident for LiCBT and HiCBT, which can be used to support treatment planning and routine outcome monitoring.

Design of a guided internet-delivered counseling intervention for test anxiety

Test anxiety is a widespread challenge among university students. Online treatment facilitates a wider distribution and easier access to treatment. In a naturalistic study, a guided internet-delivered counseling program for test anxiety in university students was created and reviewed. Students suffering from self-reported test anxiety were recruited at a university counseling center (n = 40). Test anxiety and psychological distress were measured pre and post treatment. The test anxiety online treatment was administered in six modules over a period of 6 weeks. Therapist contact via written message in the online platform was included once a week. There were two personal contacts before and after treatment. A paired t-test showed a significant reduction of test anxiety (n = 32, p < .01). There was as well a significant reduction of general psychological distress and general anxiety (n = 31, p < .05). Participants were largely satisfied with the helpfulness and composition of the program. Thus, a guided internet-delivered treatment program is an effective treatment for reducing test anxiety in university students.

Latent variable mixture modelling and individual treatment prediction

Understanding which groups of patients are more or less likely to benefit from specific treatments has important implications for healthcare. Many personalised medicine approaches in mental health employ variable-centred approaches to predicting treatment response, yet person-centred approaches that identify clinical profiles of patients can provide information on the likelihood of a range of important outcomes. In this paper, we discuss the use of latent variable mixture modelling and demonstrate its use in the application of a patient profiling algorithm using routinely collected patient data to predict outcomes from psychological treatments. This validation study analysed data from two services, which included n = 44,905 patients entering treatment. There were different patterns of reliable recovery, improvement and clinical deterioration from therapy, across the eight profiles which were consistent over time. Outcomes varied between different types of therapy within the profiles: there were significantly higher odds of reliable recovery with High Intensity therapies in two profiles (32.5% of patients) and of reliable improvement in three profiles (32.2% of patients) compared with Low Intensity treatments. In three profiles (37.4% of patients) reliable recovery was significantly more likely if patients had CBT vs Counselling. The developments and potential application of latent variable mixture approaches are further discussed.

Modelling changes in anxiety and depression during low-intensity cognitive behavioural therapy: An application of growth mixture models

OBJECTIVES

Research largely supports the clinical effectiveness of low-intensity cognitive behavioural therapy (LICBT) for mild-to-moderate anxiety and depression, delivered by psychological well-being practitioners (PWPs). Knowledge regarding the predictors of treatment response, however, is relatively limited. The primary aim of this study was to model the heterogeneity in longitudinal changes in anxiety and depression throughout LICBT provided by PWPs in Northern Ireland (NI), and to explore associations between pre-treatment variables and differences in treatment response.

METHODS

Growth mixture modelling (GMM) techniques were employed to examine changes in psychological status in clients (N = 253) over the first six sessions of treatment, to identify divergent early response trajectories. A series of pre-treatment variables were used to predict class membership using chi-square tests and binary logistic regression models.

RESULTS

There was one class representing improvement and one representing no improvement for both anxiety and depression. Class membership was predictive of treatment outcome. Pre-treatment variables associated with less improvement included unemployment, risk of suicide, neglect of self or others, using medication, receiving previous or concurrent treatments, a longer duration of difficulties, and comorbidities.

CONCLUSIONS

Findings indicate most of the sample populated an 'improvers' class for both depression and anxiety. Pre-treatment variables identified as predictive of poor treatment response may need to be considered by practitioners in potential triage referral decision policies, supporting cost-effective and efficient services. Further research around predictors of clinical outcome is recommended.

PRACTITIONER POINTS

Most of the sample belonged to an 'improvers' class. Several pre-treatment variables predicted poor treatment response (unemployment, suicide risk, neglect, medication, previous or concurrent treatments, longer duration of difficulties, and comorbidities). Few studies have utilized GMM to determine predictors of outcome following LICBT Regarding pre-treatment variables, the possibility of self-report bias cannot be excluded. The time period was relatively short, although represented the optimum number of sessions recommended for LICBT. The lack of a control group and random allocation were the main limitations.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

Attentional Control as a Predictor of Response to Psychological Treatment for Depression and Relapse up to 1 year After Treatment: A Pilot Cohort Study

BACKGROUND

Identifying depressed patients unlikely to reach remission and those likely to relapse after reaching remission is of great importance, but there are few pre-treatment factors that can help clinicians predict prognosis and together these explain relatively little variance in treatment outcomes. Attentional control has shown promise in studies to date, but has not been investigated prospectively in routine clinical settings with depressed patients.

AIMS

This study aimed to pilot the use of a brief self-report measure of attentional control in routine care and investigate the associations between attentional control, psychological treatment response and relapse to depression up to 1 year post-treatment.

METHOD

Depressed patients were recruited from two primary care psychological treatment (IAPT) services and completed the Attentional Control Scale (ACS) alongside routine symptom measures at every therapy session. Participants were tracked and followed up for 1 year post-treatment.

RESULTS

Baseline ACS scores were associated with remission and residual depressive symptoms post-treatment, and relapse within 12 months of ending treatment, all independent of pre-treatment depressive symptom severity, and the latter also independent of residual symptoms.

CONCLUSION

A self-report measure of attentional control can potentially be used to predict levels of depressive symptoms post-treatment and can contribute to predicting risk of relapse to depression in IAPT services, without affecting rates of therapy completion/drop-out or data completion of standard IAPT measures. However, this pilot study had a small overall sample size and a very small number of observed relapses, so replication in a larger study is needed before firm conclusions can be made.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 13 RCTs were found to meet inclusion criteria. The Dep-GP database was formed from the 6271 participants. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

Perspektiven einer evidenzbasierten und personalisierten Psychotherapie

Zusammenfassung. Theoretischer Hintergrund: Der Diskurs um eine evidenz-basierte und personalisierte (bzw. „Precision“) Medizin sowie zur Umsetzung von Evaluation und Qualitätssicherung hat in den letzten Jahren auch Einfluss auf die Psychotherapieforschung genommen. Dies gilt in Bezug auf die patientenspezifische Auswahl von Behandlungen (u. a. personalisierte Vorhersagen) als auch für die dynamische Anpassung von Interventionen im Therapieverlauf (adaptive Indikation, Feedback, Problemlösetools). Fragestellung und Methode: Im Bereich der differentiellen Indikation sind mittlerweile unterschiedliche Algorithmen („machine learning“) und Netzwerkmodelle zur Vorhersage erprobt worden. Für eine empirisch gestützte adaptive Indikation bilden insbesondere die Studien zum psychometrischen Feedback sowie die Entwicklung von Problemlösetools für Risikopatient_innen die Grundlage. Ergebnisse: Diese Grundlagenforschung war die Basis für die Entwicklung eines Entscheidungssystems (Trierer Therapie Navigator, TTN) zur Vorhersage der optimalen Behandlungsstrategie und des Abbruchrisikos. Darüber hinaus enthält der TTN ein adaptives Modellierungselement des Behandlungsverlaufs. Es können damit Risikopatienten für einen Behandlungsmisserfolg identifiziert und Behandlungsoptimierungen über Problemlösetools unterstützt werden. Schlussfolgerungen: In vorliegender Arbeit werden zentrale neue Ansätze einer evidenz-basierten und personalisierten Psychotherapie zusammenfassend dargestellt sowie die Anwendung in der klinischen Praxis diskutiert.

Revisiting the Cognitive Model of Depression: The Role of Expectations

The cognitive model of depression was highly stimulating for a better understanding and development of treatment for depression. However, the concept of “cognition” is rather broad and unspecific, and we suggest to focus on the cognitive subset of expectation.

We conducted a narrative review on the role of expectations, and present an expectation-focused model of explaining why depression tends to persist despite the occurrence of positive events.

Several results from basic neuroscience to effects in clinical interventions indicate that expectations play a special role not only for the understanding of the development of mental disorders and the effects of treatment approaches, but especially for an improved understanding of the persistence of mental disorders. If expectations are a major mechanism of depression, the treatment of depression must maximize the violation of dysfunctional expectations. We also introduce the concept of immunization that describes any cognitive or behavioral strategies to reduce the effect of expectation violation experiences, and hereby contributing to expectation maintenance despite expectation contradicting events. We postulate that the development of immunization strategies could help to better understand the transition from episodic to chronic depression.

While in early periods of depression development, a focus on expectation change might be sufficient in treatment, the treatment of patients with chronic depression requires addressing these cognitive and behavioral immunization strategies more intensively. Further implications for treatment and research are outlined that are derived from this balance between expectation violation and cognitive immunization in depression.

A focus on “expectations” helps to better understand the maintenance of depression

We offer a model that explains why depression persists even in the presence of positive experiences.

Many psychological treatments focus on the violation of negative expectations, but cognitive immunization can hinder treatment success.

We suggest strategies on how to improve psychological treatments for depression by maximizing expectation violation, and minimizing cognitive immunization.

A prognostic index for long-term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) has a highly recurrent nature. After successful treatment, it is important to identify individuals who are at risk of an unfavorable long-term course. Despite extensive research, there is no consensus yet on the clinically relevant predictors of long-term outcome in MDD, and no prediction models are implemented in clinical practice. The aim of this study was to create a prognostic index (PI) to estimate long-term depression severity after successful and high quality acute treatment for MDD.

METHODS

Data come from responders to cognitive therapy (CT) and interpersonal psychotherapy (IPT) in a randomized clinical trial (n = 85; CT = 45, IPT = 40). Primary outcome was depression severity, assessed with the Beck Depression Inventory II, measured throughout a 17-month follow-up phase. We examined 29 variables as potential predictors, using a model-based recursive partitioning method and bootstrap resampling in conjunction with backwards elimination. The selected predictors were combined into a PI. Individual PI scores were estimated using a cross-validation approach.

RESULTS

A total of three post-treatment predictors were identified: depression severity, hopelessness, and self-esteem. Cross-validated PI scores evidenced a strong correlation (r = 0.60) with follow-up depression severity.

CONCLUSION

Long-term predictions of MDD are multifactorial, involving a combination of variables that each has a small prognostic effect. If replicated and validated, the PI can be implemented to predict follow-up depression severity for each individual after acute treatment response, and to personalize long-term treatment strategies.