Predicting response to a smartphone-based cognitive-behavioral therapy for body dysmorphic disorder.

BACKGROUND: Body dysmorphic disorder (BDD) is a severe, chronic disorder if untreated. Smartphone cognitive behavioral therapy (CBT) for BDD is efficacious and can reduce key treatment barriers (e.g., lack of clinicians, cost, stigma). While promising, little is known about who is more or less likely to benefit from this approach. METHODS: This is a secondary data analysis of a randomized, waitlist-controlled trial of smartphone CBT for BDD. Participants (N = 80) were recruited nationally and randomized to receive a 12-week, coach-guided CBT for BDD app, either immediately or after a 12-week waitlist. The main outcome for this analysis was BDD severity (BDD-YBOCS) over time (baseline, week 6, week 12) during the active app use phase in each randomized group (n = 74). Secondary outcomes included treatment response (≥30 % reduction in BDD-YBOCS) and remission (total BDD-YBOCS ≤16) at end-of-treatment. RESULTS: Immediate (vs. delayed) CBT predicted better outcomes (symptom improvement), as did gender identity (symptom improvement), higher baseline treatment credibility and expectancy (response, remission), lower baseline BDD severity (remission), and sexual minority status (vs. heterosexual; response, remission). LIMITATIONS: Limitations include the relatively small sample, drop-out rate of 22 %, and limited gender and racial-ethnic diversity. CONCLUSIONS: These results highlight a potential advantage of smartphone CBT in historically marginalized populations, and the importance of efforts to hasten treatment access, bolster confidence in the treatment at treatment onset, and develop stratified care models to optimize treatment allocation and efficacy.

Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks.

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process.