Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of medical Internet research
Virtual Exposure With Response Prevention for Obsessive-Compulsive Disorder: Randomized Controlled Trial
BACKGROUND: Despite being highly effective and guideline-recommended for obsessive-compulsive disorder, exposure with response prevention is often underused due to barriers from both therapists and patients. Virtual reality may help address these barriers. OBJECTIVE: This study aimed to assess the efficacy and acceptance of virtual exposure with response prevention (VERP) in patients with contamination obsessive-compulsive disorder (conOCD) and checking obsessive-compulsive disorder (checkOCD) and to identify factors influencing treatment success. METHODS: Within a randomized controlled trial, 80 participants (40 with conOCD and 40 with checkOCD) were allocated to an intervention group, which received outpatient care as usual (CAU) supplemented by 6 weekly VERP sessions (VERP group), or to a control group receiving CAU alone (CAU group). Assessments occurred at baseline (t0), postintervention (t1), and at 3-month follow-up (t2). The reduction in obsessive-compulsive symptoms, as measured with the Yale-Brown Obsessive-Compulsive Scale, served as the primary outcome. RESULTS: Overall, obsessive-compulsive symptoms showed no greater improvement in the VERP group compared to the CAU group from t0 to t1 or t2 (ηp2=0.003-0.014). In exploratory subsample analyses, we observed a trend toward an interaction effect, showing greater reduction in obsessions among participants with checkOCD from t0 to t1 (F1,52=3.91; P=.05; ηp2=0.070) and greater anxiety reduction among participants with conOCD from t0 to t2 (F1,45=4.11; P=.05; ηp2=0.084). Exploratory moderation analysis suggested that adding VERP to CAU was more effective for those in the VERP group who had not prematurely discontinued prior psychological treatment and those with checking behavior. Sense of presence was only moderate, unrelated to outcome, and patient satisfaction with VERP was positive overall. CONCLUSIONS: While overall symptom reduction did not significantly differ between groups, exploratory analyses suggested trend-level differences in treatment response across obsessive-compulsive disorder subtypes and patient characteristics. These findings should be interpreted cautiously and are best viewed as hypothesis-generating.
28 May 2026
Read appraisal →Translational psychiatry
Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial
Cognitive Bias Modification - Interpretation (CBM-I) aims to alter maladaptive interpretations in social anxiety, yet effects are often small and outcome measures are diverse. Although CBM-I has shown promise, its underlying mechanisms remain unclear and integration with psychophysiological and neural measures has been limited. In this randomized controlled trial, eighty-eight participants with high levels of social anxiety completed two lab sessions, an online training in between, and online follow-up. Participants filled out questionnaires, completed interpretation bias tasks, and underwent neuro-psychophysiological assessments. Active CBM-I trained positive resolutions of ambiguous social scenarios, while the sham version used neutral scenarios. The primary outcome, i.e., scores on the Liebowitz Social Anxiety Scale (LSAS), decreased across time in both groups, without group differences. However, the Brief Fear of Negative Evaluation decreased only in the active group. Interpretation bias shifted more strongly toward positive outcomes in the active group. Autonomic measures confirmed sensitivity to stress induction but did not differentiate between conditions. Electrophysiological results paralleled subjective ratings, as participants exhibited ambivalent responses to socially relevant stimuli but clearly differentiated responses toward neutral stimuli. Baseline correlations indicated strong convergence across self-report and interpretation tasks. Mediation analyses showed that reductions in negative interpretations mediated the effect of the training group on LSAS scores at follow-up. These findings identify interpretation bias as a modifiable mechanism underlying social anxiety and underscore its role as a transdiagnostic marker. Targeting interpretation bias through easily accessible and applicable online interventions may strengthen preventive and therapeutic approaches for social anxiety and related disorders.
25 May 2026
Read appraisal →Journal of affective disorders
Right orbitofrontal cortex rTMS targets anxiety, not depressive, symptoms in first-episode schizophrenia.
BACKGROUND: Anxiety is highly prevalent and undertreated in first-episode schizophrenia(FES), but repetitive transcranial magnetic stimulation(rTMS) data for this comorbidity are scarce. The orbitofrontal cortex(OFC) is a key anxiety-regulating node, supporting its potential as a target. This study aimed to explore 1-Hz right OFC-rTMS's symptom-specific effects on FES mood. METHODS: This is a secondary analysis of a randomized controlled trial. Participants were drug-naive FES patients randomized to the active rTMS group(n = 51) or sham group(n = 45). All completed 20 intervention sessions, 20 sessions of active OFC-rTMS or sham, with 4-week follow-up, initiating oral olanzapine(10-20 mg/day) concurrently with the first rTMS session. Mood symptoms were assessed using the 24-item Hamilton Depression Rating Scale(HAMD) and the 14-item Hamilton Anxiety Rating Scale(HAMA). Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale(PANSS). The main outcome was the changes in HAMD and HAMA scores from baseline to 2 weeks and 4 weeks. RESULTS: The active group showed greater PANSS total(t = -3.260, p = 0.002; Cohen's d = 0.672) and subscale improvements vs. the sham group. Repeated-measures ANOVA(controlling for covariates) revealed significant Time×Group interactions for HAMA total(F = 4.698, p = 0.010; partial η2 = 0.059) and psychic anxiety(F = 5.735, p = 0.004; partial η2 = 0.072), but not somatic anxiety. For HAMD, only anxiety/somatization(F = 8.397, p = 0.031; partial η2 = 0.099) and cognitive impairment(F = 6.240, p = 0.002; partial η2 = 0.076) showed interactions, with no specific effects on overall depressive symptoms. In the sham group, HAMD anxiety/somatization correlated with all PANSS subscales(r = 0.311-0.477, p < 0.05), but this correlation was absent in the active group(all p > 0.05). CONCLUSIONS: Right OFC-rTMS improves FES anxiety (not depression), supporting it as a targeted non-pharmacological option.
17 May 2026
Read appraisal →Journal of affective disorders
An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.
Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Although treatments such as CBT and SSRIs are effective, access remains limited. This exploratory, randomized controlled trial evaluated the effectiveness of an AI-powered mental health app (PATH) in reducing symptoms of anxiety and depression. A total of 316 UK-based participants (aged 19-70) were randomized to either the intervention group (PATH) or a control group (NHS self-help website). The intervention provided evidence-based strategies including CBT-informed chat therapy and interactive tools. Anxiety (GAD-7) and depression (PHQ-9) scores were measured at baseline, two, eight, and twelve weeks. Of the 316 randomized participants, 235 completed the post-intervention assessment (33.0% attrition in the intervention group vs. 18.5% in the control group). Retention at 8-week and 12-week follow-ups was 77.4% and 54.0%, respectively. At two weeks, the intervention group showed significantly lower GAD-7 and PHQ-9 scores compared to control, with medium effect sizes. At eight weeks, those who continued using the app showed significant reductions in both anxiety and depression, while those who discontinued still showed medium improvements in anxiety. Effects were maintained at twelve weeks, with moderate to large effect sizes. Findings suggest that PATH significantly reduces anxiety and depressive symptoms, particularly with continued use. These results support the app's potential as a scalable, accessible digital intervention to address mental health treatment gaps.
16 May 2026
Read appraisal →Journal of affective disorders
Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.
BACKGROUND: Although mindfulness ecological momentary interventions (MEMI) appear effective in alleviating worry symptoms, treatment engagement remains suboptimal. Determining baseline variables of MEMI over self-monitoring placebo (SM) can inform tailored interventions for individuals with generalized anxiety disorder (GAD). METHOD: Machine meta-learning methods (ML) were applied to predict two-week engagement (log-transformed number of prompts completed) among individuals randomized to MEMI or SM (N = 110). Sixteen baseline variables comprised the predictor set: clinical, demographic, process, and executive functioning (EF) factors. Random forest using a five-fold nested cross-validation approach mitigated overfitting. X-learner meta-algorithms estimated conditional average treatment engagement (CATE). Shapley additive explanations evaluated relative importance. RESULTS: The 16-predictor model displayed strong predictive performance (R-squared [R2] = 82.7%; root mean squared error [RMSE] = 0.780; mean absolute error [MAE] = 0.512). The top-10 predictor model also yielded good predictive performance (R2 = 82.1%; RMSE = 0.547; MAE = 0.307). As predicted by the CATE analysiscate, participants had the highest treatment engagement when assigned to MEMI instead of SM (d = 1.447, p < .001). The following baseline variables predicted more engagement with MEMI over SM: lower GAD severity, inhibition response time (RT), and EF errors, higher attentional control, empathy, and verbal fluency (capitalization theory); lower mindfulness, and treatment expectancy, poorer working memory, and higher set-shifting RT (compensation model). LIMITATIONS: The small sample size, single engagement metric, and brief duration might constrain generalizability. DISCUSSION: Integrating robust ML approaches could optimally identify prescriptive predictors of engagement to brief digital mental health interventions to inform targeted treatments. TRIAL REGISTRATION: ClinicalTrials.gov ID (NCT04846777).
16 Apr 2026
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