Background: The study was designed to compare cognitive therapy (CT) with intensive behavior therapy (BT) in obsessive-compulsive disorder (OCD) and to study their change process. Methods: Sixty-five outpatients with DSM-4 OCD were randomized into 2 groups for 16 weeks of individual treatment in 3 centers. Group 1 received 20 sessions of CT. Group 2 received a BT program of 20 h in two phases: 4 weeks of intensive treatment (16 h), and 12 weeks of maintenance sessions (4 h). No medication was prescribed. Results: Sixty-two patients were evaluated at week 4, 60 at week 16 (post-test), 53 at week 26 and 48 at week 52 (follow-up). The response rate was similar in the 2 groups. The Beck Depression Inventory (BDI) was significantly more improved by CT (p = 0.001) at week 16. The baseline BDI and Obsessive Thoughts Checklist scores predicted a therapeutic response in CT, while the baseline BDI score predicted a response in BT. At week 16, only the changes in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and a scale measuring the interpretation of intrusive thoughts correlated in CT, while the changes in Y-BOCS, BDI, and interpretation of intrusive thoughts correlated in BT. Improvement was retained at follow-up without a between-group difference. The intent-to-treat analysis (last observation carried forward) found no between-group differences on obsessions, rituals and depression. Conclusions: CT and BT were equally effective on OCD, but at post-test CT had specific effects on depression which were stronger than those of BT. Pathways to improvement may be different in CT and BT. The outcomes are discussed in the light of an effect size analysis.

Embodied Conversational Agents (ECAs) are promising software to communicate with patients but no study has tested them in the diagnostic field of mental disorders. The aim of this study was 1) to test the performance of a diagnostic system for major depressive disorders (MDD), based on the identification by an ECA of specific symptoms (the MDD DSM 5 criteria) in outpatients; 2) to evaluate the acceptability of such an ECA. Patients completed two clinical interviews in a randomized order (ECA versus psychiatrist) and filled in the Acceptability E-scale (AES) to quantify the acceptability of the ECA. 179 outpatients were included in this study (mean age 46.5 ± 12.9 years, 57.5% females). Among the 35 patients diagnosed with MDD by the psychiatrist, 14 (40%) patients exhibited mild, 12 (34.3%) moderate and 9 (25.7%) severe depressive symptoms. Sensitivity increased across the severity level of depressive symptoms and reached 73% for patients with severe depressive symptoms, while specificity remained above 95% for all three severity levels. The acceptability of the ECA evaluated by the AES was very good (25.4). We demonstrate here the validity and acceptability of an ECA to diagnose major depressive disorders. ECAs are promising tools to conduct standardized and well-accepted clinical interviews.

Virtual agents have demonstrated their ability to conduct clinical interviews. However, the factors influencing patients' engagement with these agents have not yet been assessed. The objective of this study is to assess in outpatients the trust and acceptance of virtual agents performing medical interviews and to explore their influence on outpatients' engagement. In all, 318 outpatients were enroled. The agent was perceived as trustworthy and well accepted by the patients, confirming the good engagement of patients in the interaction. Older and less-educated patients accepted the virtual medical agent (VMA) more than younger and welleducated ones. Credibility of the agent appeared to main dimension, enabling engaged and non-engaged outpatients to be classified. Our results show a high rate of engagement with the virtual agent that was mainly related to high trust and acceptance of the agent. These results open new paths for the future use of VMAs in medicine.npj Digital Medicine (2020) 3:2 ; https://doi.

International audienceExcessive daytime somnolence (EDS) is defined as the inability to stay awake in daily life activities. Several scales have been used to diagnose excessive daytime sleepiness, the most widely used being the Epworth Sleepiness Scale (ESS). Sleep disorders and EDS are very common in the general population. It is therefore important to be able to screen patients for this symptom in order to obtain an accurate diagnosis of sleep disorders. Embodied Conversational Agents (ECA) have been used in the field of affective computing and human interactions but up to now no software has been specifically designed to investigate sleep disorders. We created an ECA able to conduct an interview based on the ESS and compared it to an interview conducted by a sleep specialist. We recruited 32 consecutive patients and a group of 30 healthy volunteers free of any sleep complaints. The ESS is a self-administered questionnaire that asks the subject to rate (with a pen and paper paradigm) his or her probability of falling asleep. For the purpose of our study, the ECA or real-doctor questionnaire was modified as follows: Instead of the “I” formulate, questions were asked as “Do you.” Our software is based on a common 3D game engine and several commercial software libraries. It can run on standard and affordable hardware products. The sensitivity and specificity of the interview conducted by the ECA were measured. The best results (sensibility and specificity >98%) were obtained to discriminate the sleepiest patients (ESS ≥16) but very good scores (sensibility and specificity >80%) were also obtained for alert subjects (ESS<8). ESS scores obtained in the interview conducted by the physician were significantly correlated with ESS scores obtained in the interview the ECA conducted. Most of the subjects had a positive perception of the virtual physician and considered the interview with the ECA as a good experience. Sixty-five percent of the participants felt that the virtual doctor could significantly help real physicians. Our results show that a virtual physician can conduct a very simple interview to evaluate EDS with very similar results to those obtained by a questionnaire administered by a real physician. The expected massive increase in sleep complaints in the near future likely means that more and more physicians will be looking for computerized systems to help them to diagnose their patients