Trials Of Treatments For Depression Research Articles

Factors influencing diabetes treatment satisfaction in the INtegrating DEPrEssioN and Diabetes treatmENT randomized clinical trial: A multilevel model analysis.

Patient satisfaction is associated with positive diabetes outcomes. However, there are no identified studies that evaluate both patient- and clinic-level predictors influencing diabetes care satisfaction longitudinally. Data from the INtegrating DEPrEssioN and Diabetes treatmENT trial was used to perform the analysis. We used fixed and random effects models to assess whether and how changes in patient-level predictors (treatment assignment, depression symptom severity, systolic blood pressure, body mass index, LDL cholesterol, and haemoglobin A1C) from 0 to 24 months and clinic-level predictors (visit frequency, visit cost, number of specialists, wait time, time spent with healthcare provider, and receiving verbal reminders) measured at 24 months influence diabetes care satisfaction from 0 to 24 months. Model 1 (patient-level predictors) accounted for 7% of the change in diabetes satisfaction and there was a significant negative relationship between change in depressive symptoms and care satisfaction (β = -0.23, SE = 0.12, p < 0.05). Within Model 1, 2% of the variance was explained by clinic-level predictors. Model 2 included both patient- and clinic-level predictors and accounted for 18% of the change in diabetes care satisfaction. Within Model 2, 9% of the variance was attributed to clinic-level predictors. There was also a cross-level interaction where the change in depression had less of an impact on the change in satisfaction for those who received a verbal reminder (β = -0.11, SE = 0.21, p = 0.34) compared with those who did not receive a reminder (β = -0.62, SE = 0.08, p < 0.01). Increased burden of depressive symptoms influences diabetes care satisfaction. Clinic-level predictors also significantly influence diabetes care satisfaction and can reduce dissatisfaction in primary care, specifically, reminder calls from clinic staff.

Interaction between depression and insomnia under acupuncture treatment: a secondary analysis of a randomized controlled trial

To explore the interaction between emotional and insomnia symptom improvement in comorbid depression and insomnia patients treated with optimized acupuncture. A secondary analysis was conducted on a multicenter randomized controlled trial of acupuncture treatment for comorbid depression and insomnia. One hundred and forty patients with comorbid depression and insomnia were randomly assigned to an optimized acupuncture group (70 cases, 8 cases dropped out) and a conventional acupuncture group (70 cases, 8 cases dropped out). The patients in the conventional acupuncture group were treated with acupuncture at Yintang (GV 24+), Baihui (GV 20), bilateral Hegu (LI 4) and Taichong (LR 3); the patients in the optimized acupuncture group were treated with additional acupuncture at bilateral Lieque (LU 7), Zhaohai (KI 6), and intradermal needling was applied alternately at bilateral Xinshu (BL 15) and Danshu (BL 19), Shenshu (BL 23) and Anmian (Extra). Both groups received treatments twice a week for six weeks. The Hamilton depression scale-24 (HAMD-24) and Pittsburgh sleep quality index (PSQI) scores were observed before and after treatment for both groups. Mediation analysis was used to assess the mesomeric effect of PSQI and HAMD-24 emotional factors, and latent profile analysis categorized patients into subgroups based on HAMD-24 scores, followed by between-group efficacy comparisons. The indirect effect of the optimized acupuncture on improving depression emotions through PSQI was 3.052 (95%CI [1.407, 4.697]), accounting for 77.5% of the total effect. Conversely, the indirect effect of improving PSQI through the optimized protocol on depression emotions was 0.479 (95%CI [0.024, 0.935]), representing 14.8% of the total effect. Latent profile analysis identified 3 categories based on emotional and insomnia symptoms, and statistically significant differences in efficacy favoring the optimized acupuncture protocol over the conventional acupuncture protocol were observed only in the subgroup with relatively mild depression and insomnia symptoms (PSQI: 95%CI [-10.98, -5.44], P<0.001; HAMD-24: 95%CI [-12.31, -3.73], P =0.001). The optimized acupuncture protocol primarily improves insomnia symptoms and further improves depression emotions. Its efficacy advantages are more evident in patients with mild symptoms compared with the conventional acupuncture protocol.

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

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

The Role of Social Support in Perinatal Mental Health and Psychosocial Stimulation.

Social support refers to the help someone receives emotionally or instrumentally from their social network. Poor social support in the perinatal period has been associated with increased risk for symptoms of common mental disorders, including depression and posttraumatic stress symptoms (PTS), which may impact parenting behavior. Whether social support impacts parenting behaviors, independent of mental health symptomatology, remains unclear. Among N=309 participants of the Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT Trial), a large perinatal depression and anxiety treatment trial, we explored the relations between perceived social support, perinatal depressive and PTS symptoms, and psychosocial stimulation provided by the parent in their home environment. Social support was measured at baseline using the Multidimensional Scale of Perceived Social Support (MSPSS). Perinatal depressive symptoms were measured by the Edinburgh Postnatal Depression Scale (EPDS) and PTS symptoms were measured by the Abbreviated PTSD Checklist (PCL-6) at baseline, 3-, and 6-months post-randomization. Psychosocial stimulation was assessed by the Home Observation Measurement of the Environment (HOME) when the infant was between 6 to 24 months. Using stepwise hierarchical regressions, we found: (1) perceived social support at baseline significantly predicted both depressive and PTS symptoms at 3-months post-randomization, even when controlling for baseline depressive and PTS symptoms; and (2) while neither depressive nor PTS symptoms were significantly associated with psychosocial stimulation, perceived social support at baseline was a significant predictor. Clinical implications regarding treatment of perinatal patients are discussed.

Assessment of suicidality in trials of psychological interventions for depression: a meta-analysis

Psychological interventions that are efficacious as treatments for depression could indirectly affect suicide-related outcomes. We examined suicidal thoughts and behaviours as eligibility criteria, outcomes, and adverse events across trials of psychotherapy for depression. We used a publicly available meta-analytic database developed through systematic searches (updated as of May 1, 2023) to identify randomised controlled trials in which a psychological intervention for depression was compared with an inactive or non-specific control condition in adults with depression and in which any suicide-related outcomes were reported. We also identified studies in which suicide risk was an exclusion criterion. We excluded inpatient studies and trials of unguided digital interventions or collaborative care that included a psychological component. Pairs of reviewers worked independently to select studies and extract data. In a random-effects meta-analysis with robust variance estimation, we assessed the effect of the psychological intervention on suicide outcomes in trials in which suicide was explicitly assessed as an outcome with clinical scales with established psychometric properties. Risk of bias was assessed with the Cochrane risk-of-bias tool (version 2). Of the 469 randomised trials we identified in which a psychological intervention was compared with an inactive control in people with depression, 251 excluded people judged at risk of suicide. Any assessment of suicide was included in only 45 trials, 12 of which assessed suicidal ideation or risk as an outcome. These 12 trials included 3930 participants, 2795 (71%) of whom were female and 1135 (29%) of whom were male; data for age and ethnicity were not consistently reported. Psychological interventions for depression were associated with a small reduction in suicidal ideation and risk in 11 trials (one trial reported only follow-up data) after the intervention (standardised mean difference -0·31 [95% CI -0·60 to -0·03]) but not at follow-up (-0·49 [-1·31 to 0·32]). Suicide-related adverse events were reported in 25 trials, and suicide-related serious adverse events (eg, suicide attempts, deaths by suicide) were reported in 13 trials. Heterogeneity was substantial across all analyses, and prediction intervals crossed zero. Trials of psychological interventions for depression rarely report assessments of suicide. Psychological interventions might reduce suicidal ideation in patients with depression, but more randomised controlled trials are required to clarify this effect. Monitoring and reporting of suicide-related adverse events should be improved in trials of psychological interventions for depression, and future trials should incorporate outcomes related to suicidal thoughts or behaviours. None. For the Spanish translation of the abstract see Supplementary Materials section.

54 Cognitive Profiles of Older Adults with Depression in Psychotherapy Trials: A Scoping Review

Objective:Cognitive impairment is often comorbid with depression and anxiety, and the cognitive status of older adult patients can drastically impact depression treatment outcomes. The cognitive status of these patients invariably changes psychological treatment approaches that otherwise are viable and feasible in older adults. For example, although cognitive behavioral therapy is effective in treating cognitively intact patients with depression, it often relies on executive function (such as flexible thinking and problem solving) and other cognitive abilities that are impaired in patients with comorbid cognitive impairment. Practically, this results in unstandardized modifications to psychotherapy that may impact the fidelity—and thus effectiveness—of treatment. It is important to assess and classify cognitive dysfunction in depression treatment-seeking older adults in trials. This can help generalize research findings and identify potential barriers in transferring psychotherapeutic approaches for older adults with depression from treatment trials to practical clinical use, particularly in hard-to-treat populations with comorbid cognitive impairment.Participants and Methods:A systematic literature search was conducted in PubMed for the period 2000-2022. Study inclusion criteria was operationalised as follows: participants were identified as older adults (55 years and older), their primary psychiatric diagnosis was depression, and the study was a trial for depression treatment. Key search terms included: depression, treatment, psychotherapy, therapy, counseling, intervention, older adult, senior, late-life, elder, aged, clinical trial, and randomized controlled trial.Results:An initial search of the key terms returned 3,972 articles. 178 of these articles were subject to full text review. Of those, 45 articles met inclusion criteria. Overall study quality was acceptable. A portion of treatment trials did not assess for cognitive functioning. A majority of the articles excluded patients with cognitive impairment, with no further elaboration on the potential impact of cognitive functioning on treatment outcomes. A smaller portion of studies were more inclusive of the cognitive range of patient participants; however, they did not comment on the cognitive heterogeneity of their samples. Only three studies used a more extensive neuropsychological battery to examine cognitive profiles of patient participants. However, two of these studies also excluded individuals that fell below the cognitively intact range based on brief cognitive screening measures. Of the few studies that examined depression treatment in cognitively impaired and dementia patient populations, two trials examined cognitive functioning as a predictor or moderator of depression treatment outcome.Conclusions:Given that cognitive status can significantly impact depression treatment outcomes for older adults, there is a shocking dearth of inclusion of cognitively impaired patients in depression treatment clinical trials. Moreover, the limited studies that examined depression treatment in cognitively impaired populations, there is a lack of comprehensive cognitive assessment, and lack of exploration on how different types of cognitive dysfunction may contribute to variable depression treatment response. Future depression treatment trials in older adults should expand to include a variety of cognitive functioning ranges, as well as a more detailed assessment of how specific cognitive domains may impact treatment outcomes.

Adaptation and outcomes of a lay-guided mental health self-care model: Results of six trials

ObjectiveTo synthesize results of six controlled trials of self-care interventions for depression and/or anxiety, focusing on five trials in which lay guidance was compared to self-directed use of the same self-care tools. MethodsThe trials were conducted in Canada in different target populations. Self-care tools were adapted to each population. Guidance was provided in 3–15 calls over a period of 6–26 weeks. Depression and/or anxiety were assessed at follow-up (6–26 weeks). Pooled analyses used a meta-analytic approach. Engagement with the self-care tools was compared using the standardized difference or Cohen's d effect size. ResultsIn studies with homogeneous outcomes (three for depression, four for anxiety), the pooled effect sizes of guidance vs. self-directed use of the self-care tools were 0.36 (95% CI 0.10, 0.62, N = 235) for depression and 0.21 (95% CI -0.03, 0.44, N = 285) for anxiety. Guidance consistently led to greater engagement with the tools. ConclusionsThe intervention model is a potentially sustainable and accessible alternative to professionally guided self-care for people with mild-moderate depression. Factors which may have limited implementation success include: co-interventions, reduced number of guide calls (3 vs 6 or more), and delivery to dyads (patient-caregiver).

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies

BackgroundMajor depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. MethodsWe analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. ResultsA 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. ConclusionIt is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

The effects of advanced factor analysis approaches on outcomes in randomised trials for depression: protocol for secondary analysis of individual participant data.

Modern psychometric methods make it possible to eliminate nonperforming items and reduce measurement error. Application of these methods to existing outcome measures can reduce variability in scores, and may increase treatment effect sizes in depression treatment trials. We aim to determine whether using confirmatory factor analysis techniques can provide better estimates of the true effects of treatments, by conducting secondary analyses of individual patient data from randomised trials of antidepressant therapies. We will access individual patient data from antidepressant treatment trials through Clinicalstudydatarequest.com and Vivli.org, specifically targeting studies that used the Hamilton Rating Scale for Depression (HRSD) as the outcome measure. Exploratory and confirmatory factor analytic approaches will be used to determine pre-treatment (baseline) and post-treatment models of depression, in terms of the number of factors and weighted scores of each item. Differences in the derived factor scores between baseline and outcome measurements will yield an effect size for factor-informed depression change. The difference between the factor-informed effect size and each original trial effect size, calculated with total HRSD-17 scores, will be determined, and the differences modelled with meta-analytic approaches. Risk differences for proportions of patients who achieved remission will also be evaluated. Furthermore, measurement invariance methods will be used to assess potential gender differences. Our approach will determine whether adopting advanced psychometric analyses can improve precision and better estimate effect sizes in antidepressant treatment trials. The proposed methods could have implications for future trials and other types of studies that use patient-reported outcome measures.

Psychedelic medicines for end-of-life care: Pipeline clinical trial review 2022.

People with terminal illnesses often experience psychological distress and associated disability. Recent clinical trial evidence has stimulated interest in the therapeutic use of psychedelics at end of life. Much uncertainty remains, however, mainly due to methodological difficulties that beset existing trials. We conducted a scoping review of pipeline clinical trials of psychedelic treatment for depression, anxiety, and existential distress at end of life. Proposed, registered, and ongoing trials were identified from 2 electronic databases (ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform). Recent reviews and both commercial and non-profit organization websites were used to identify additional unregistered trials. In total, 25 studies were eligible, including 13 randomized controlled trials and 12 open-label trials. Three trials made attempts beyond randomization to assess expectancy and blinding effectiveness. Investigational drugs included ketamine (n=11), psilocybin (n=10), 3,4-methylenedioxymethamphetamine (n=2), and lysergic acid diethylamide (n=2). Three trials involved microdosing, and fifteen trials incorporated psychotherapy. A variety of onging or upcoming clinical trials are expected to usefully extend evidence regarding psychedelic-assisted group therapy and microdosing in the end-of-life setting. Still needed are head-to-head comparisons of different psychedelics to identify those best suited to specific indications and clinical populations. More extensive and rigorous studies are also necessary to better control expectancy, confirm therapeutic findings and establish safety data to guide the clinical application of these novel therapies.

A critical evaluation of QIDS-SR-16 using data from a trial of psilocybin therapy versus escitalopram treatment for depression.

In a recent clinical trial examining the comparative efficacy of psilocybin therapy (PT) versus escitalopram treatment (ET) for major depressive disorder, 14 of 16 major efficacy outcome measures yielded results that favored PT, but the Quick Inventory of Depressive Symptomatology, Self-Report, 16 items (QIDS-SR16) did not. The present study aims to (1) rationally and psychometrically account for discrepant results between outcome measures and (2) to overcome psychometric problems particular to individual measures by re-examining between-condition differences in depressive response using all outcome measures at item-, facet-, and factor-levels of analysis. Four depression measures were compared on the basis of their validity for examining differences in depressive response between PT and ET conditions. Possible reasons for discrepant findings on the QIDS-SR16 include its higher variance, imprecision due to compound items and whole-scale and unidimensional sum-scoring, vagueness in the phrasing of scoring options for items, and its lack of focus on a core depression factor. Reanalyzing the trial data at item-, facet-, and factor-levels yielded results suggestive of PT's superior efficacy in reducing depressed mood, anhedonia, and a core depression factor, along with specific symptoms such as sexual dysfunction. Our results raise concerns about the adequacy of the QIDS-SR16 for measuring depression, as well as the practice of relying on individual scales that tend not to capture the multidimensional structure or core of depression. Using an alternative approach that captures depression more granularly and comprehensively yielded specific insight into areas where PT therapy may be particularly useful to patients and clinicians.

Depression trial results: A cross-sectional study of ClinicalTrials.gov

Published clinical trials represent a subsample of the objective information needed to appraise treatments for depression. We characterize the extent of selective and delayed reporting in a systematic review (PROSPERO #CRD42020173606) of depression trial results registered on ClinicalTrials.gov. Inclusion criteria were studies registered on ClinicalTrials.gov with depression as the condition, had enrolled ages 18 and over, were completed between January 1, 2008 and May 1, 2019, and had posted results by February 1, 2022. Cox regression analyses of time to result posting from registration and from study completion included enrollment as a covariate. Among 442 protocols, median result posting occurred over two years after study completion and five years after registration. Among protocols with incomplete results, effect sizes (d or W) were calculated for 134 protocols. Median effect sizes for protocols with incomplete results were small (0.16, 95% CI 0.08, 0.21). For 28% of protocols, observed effects were contrary to the expected direction. Between-group effect size calculations were based on post-treatment data as pre-treatment data were inconsistently provided. Although drug and device trials in the U.S. are required to register on ClinicalTrials.gov, compliance is imperfect, and submissions are not peer reviewed. For depression treatment trials, long intervals between study completion and posting of results are common. Further, investigators often fail to report the results of statistical tests. Failure to post trial results in a timely manner and omission of statistical test reporting may lead to overestimates of treatment effects in systematic literature reviews.

Stimulating human prefrontal cortex increases reward learning

Work in computational psychiatry suggests that mood disorders may stem from aberrant reinforcement learning processes. Specifically, it has been proposed that depressed individuals believe that negative events are more informative than positive events, resulting in higher learning rates from negative outcomes (Pulcu and Browning, 2019). In this proof-of-concept study, we investigated whether transcranial direct current stimulation (tDCS) applied to dorsolateral prefrontal cortex, as commonly used in depression treatment trials, might change learning rates for affective outcomes. Healthy adults completed an established reinforcement learning task (Pulcu and Browning, 2017) in which the information content of reward and loss outcomes was manipulated by varying the volatility of stimulus-outcome associations. Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) increased learning rates specifically for reward outcomes. The effects of prefrontal tDCS were cognitive state-dependent: tDCS applied during task performance increased learning rates for wins; tDCS applied before task performance decreased both win and loss learning rates. A replication study confirmed the key finding that tDCS to DLPFC during task performance increased learning rates specifically for rewards. Taken together, these findings demonstrate the potential of tDCS for modulating computational parameters of reinforcement learning that are relevant to mood disorders.

Machine learning prediction will be part of future treatment of depression.

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.

Transdiagnostic treatment of depression and anxiety: a meta-analysis.

In the past 10 years an increasing number of randomised trials have examined the effects of transdiagnostic treatments of patients with depression or anxiety. We conducted the first comprehensive meta-analysis of the outcomes of this emerging field. We used the searches in PubMed, PsychINFO, Embase and the Cochrane library of an existing database of randomised trials of psychological interventions for depression to identify studies comparing a transdiagnostic treatment of patients with depression or anxiety with a control group (deadline 1 January 2022). We conducted random-effects meta-analyses and examined the effects on depression and anxiety at the short and longer term. We included 45 randomised controlled trials with 51 comparisons between a psychotherapy and a control group and 5530 participants. Thirty-five (78%) studies were conducted in the last 10 years. The overall effect size was g = 0.54 (95% CI 0.40-0.69; NNT = 5.87), with high heterogeneity (I2 = 78; 95% CI 71-83), and a broad PI (-0.31-1.39). The effects remained significant in a series of sensitivity analyses, including exclusion of outliers, adjustment for publication bias, for studies with low risk of bias, and in multilevel analyses. The results were comparable for depression and anxiety separately. At 6 months after randomisation the main effects were still significant, but not at 12 months, although the number of studies was small. Transdiagnostic treatments of patients with depression or anxiety are increasingly examined and are probably effective at the short term.

ADDRESSING THE MENTAL HEALTH NEEDS OF HOMEBOUND OLDER ADULTS IN AGING SERVICE SETTINGS

Abstract Despite significant and urgent mental health needs among low-income homebound older adults, the existing mental health service systems’ reach for these older adults is almost nonexistent. Given the current and projected geriatric mental health workforce shortages, innovative approaches are needed to better serve these underserved older adults. This presentation will focus on a series of randomized clinical trials that tested acceptable and feasible mental health service delivery models for homebound older adults who are served by aging service network agencies that provide hot meals and case management. Findings from the real-world effectiveness trials of tele-delivered behavioral activation treatment for depression and loneliness by bachelor’s-level lay counselors who are embedded in aging service agencies will be shared. Ways to promote a wider adoption of scalable and sustainable mental health service delivery models for homebound older adults will be discussed.

Characterizing Measurement-Based Care Implementation Using Therapist Report

ABSTRACT The effectiveness of measurement-based care (MBC), an evidence-based practice that uses regularly collected assessment data to guide clinical decision-making, is impacted by whether and how therapists use information from MBC tools in treatment. Improved characterization of how therapists use MBC in treatment sessions with youth is needed to guide implementation and understand variability in MBC effectiveness. To meet this need, this study examined therapists’ sharing and discussion of MBC in treatment sessions. Thirty therapists were randomly assigned to the MBC condition as part of a comparative effectiveness trial of treatments for adolescent anxiety and depression. A qualitative content analysis was conducted on therapists’ written explanations of changes made to the session based on the MBC data. Therapists reported sharing data with youth and caregivers in an average of 34.6% and 27.4% of sessions, respectively. Therapists reported incorporating MBC data in an average of 21.1% of sessions. When data were used, therapists predominately focused changes on short-term (e.g. current symptoms, treatment skill) rather than long-term (e.g. symptom progress, treatment goals) decision-making. Therapists inconsistently used MBC data, highlighting the need for improved training in and monitoring of how therapists use MBC in session to guide collaborative treatment decision-making with youth and caregivers.

Alliance rupture profiles by personality disorder pathology in psychotherapy for depression: Tendencies, development, and timing.

Clinical and theoretical considerations presume that patients with different personality disorder (PD) clusters will be associated with distinct alliance rupture profiles; however, there is scarce empirical literature examining this. The present study adopted a systematic framework for investigating profiles of alliance ruptures for individuals belonging to each of the three PD clusters. The sample consisted of 94 patients from a randomized controlled trial for treatment of depression. PD cluster features were assessed at intake and ruptures were assessed across treatment. Three sets of multilevel analyses were conducted to test differences between the PD clusters in the general tendency to show a rupture profile, rupture development throughout the treatment, and timing of predicting ruptures by PD within sessions. The three clusters were associated with distinct profiles of alliance ruptures. Clusters A and B were characterized by a general tendency to show more withdrawal and confrontation ruptures. Cluster A had a greater decrease in confrontation ruptures over the course of treatment, while cluster B had a greater decrease in withdrawal ruptures. Cluster C was characterized by a general tendency to show fewer withdrawal and confrontation ruptures, with a greater increase in both ruptures over the course of treatment. For withdrawal ruptures, the differences between clusters were evident from the beginning of sessions, whereas for confrontation ruptures, there was less of a difference between beginning and end of sessions. The distinct profiles of alliance ruptures for each PD cluster may contribute to progress towards tailoring treatment to individuals with PDs.

Non-pharmacological interventions for depression among survivors of adverse childhood experiences: A meta-analysis

Adverse childhood experiences (ACEs) are associated with various mental health problems such as depression, anxiety, drug abuse, and suicide-related behaviors. Yet, it is largely unknown if non-pharmacological interventions are beneficial in improving mental health among ACEs survivors. The purposes of the current meta-analysis were to: (1) evaluate the efficacy of non-pharmacological interventions in reducing depressive symptom severity among those exposed to ACEs, and, (2) estimate the pooled effect size of these interventions. We searched PubMed, CINAHL, and PsycINFO for randomized controlled trials of psychological interventions for depression among ACEs survivors. Two reviewers independently extracted article data and evaluated the quality of studies using the Cochrane risk-of-bias criteria. We estimated the pooled effect sizes using a random-effects model. We also performed sensitivity tests using a meta-regression and sub-group analyses. Using the pooled data of 1624 individuals from 20 unique RCTs, we found that non-pharmacological intervention had a medium to large effect size in reducing depressive symptom severity compared to control conditions (Hedges’ g = 0.714, 95% CI [0.46, 0.97]). Such efficacy was sustained over the shorter-term follow-up (0 to 6 months; Hedges’ g = 0.23, 95% CI [0.04, 0.49]). Although there was an indication that the efficacy may be sustained in the longer term (12 months or more; Hedges’ g = 0.53, 95% CI: [−0.17, 1.22]), this finding was not statistically significant. Cognitive approaches had a larger effect size than noncognitive interventions. In conclusion, non-pharmacological interventions are efficacious options for treating depression among individuals with a history of adverse childhood experiences.

Association of Single-blind Placebo Run-in Periods With the Placebo Response in Randomized Clinical Trials of Antidepressants

Single-blind placebo run-in (PRI) periods are common in randomized clinical trials (RCTs) of treatment for depression. They aim to increase sensitivity to detect drug effects; however, the association of PRI periods with study outcomes remains unclear. This is concerning given the costs of PRI periods to patients and investigators. To examine the association of the use of PRI periods with the placebo response, drug response, and drug-placebo difference among RCTs of antidepressants. MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and PsycINFO, as well as repositories of unpublished studies, were systematically searched up to July 2021. Included studies were double-blind, placebo-controlled RCTs of antidepressant medication among adults with depressive disorders. Data were extracted into a coding sheet, including the characteristics of studies, the characteristics of PRI periods, and the outcomes of studies. Study outcomes were the primary depression symptom measure reported by the RCT. These outcomes were used to calculate effect sizes (Hedges g) of the within-group drug response and placebo response as well as the drug-placebo difference. Random-effects meta-analysis was used to calculate effect sizes, and subgroup analyses were used to compare outcomes depending on use of PRI periods. A total of 347 trials (representing 89 183 participants) were included; 174 studies (50%) reported using a single-blind PRI period. Response outcome data were available for 189 studies. Studies using PRI periods reported a smaller placebo response (g = 1.05 [95% CI, 0.98-1.11]; I2 = 82%) than studies that did not use a PRI period (g = 1.15 [95% CI, 1.09-1.21]; I2 = 81%; P = .02). Subgroup analysis showed a larger drug response size among studies that did not use a PRI period (g = 1.55 [95% CI, 1.49-1.61]; I2 = 85%) than those that did use a PRI period (g = 1.42 [95% CI, 1.36-1.48]; I2 = 81%; P = .001). The drug-placebo difference did not differ by use of PRI periods (g = 0.33 [95% CI, 0.29-0.38]; I2 = 47% for use of a PRI period vs g = 0.34 [95% CI, 0.30-0.38]; I2 = 54% for no use of PRI periods; P = .92). The likelihood of response to drug vs placebo also did not differ between studies that used a PRI period (odds ratio, 1.89 [95% CI, 1.76-2.03]) and those that did not use a PRI period (odds ratio, 1.77 [95% CI, 1.65-1.89]; P = .18). This study suggests that RCTs using PRI periods yield smaller within-group changes across both placebo and drug groups compared with RCTs without PRI periods. The reduction in effect size across groups was equivalent in magnitude. Consequently, PRI studies do not observe larger drug-placebo differences, suggesting that they do not increase trial sensitivity. As such, given the resources and probable deception required and risk to external validity, the practice of using PRI periods in RCTs of antidepressants should be ended.