Conversational Agents for depression screening: a systematic review

Objective: This work explores the advances in conversational agents aimed at the detection of mental health disorders, and specifically the screening of depression. The focus is put on those based on voice interaction, but other approaches are also tackled, such as text-based interaction or embodied avatars. Methods: PRISMA was selected as the systematic methodology for the analysis of existing literature, which was retrieved from Scopus, PubMed, IEEE Xplore, APA PsycINFO, Cochrane, and Web of Science. Relevant research addresses the detection of depression using conversational agents, and the selection criteria utilized include their effectiveness, usability, personalization, and psychometric properties. Results: Of the 993 references initially retrieved, 36 were finally included in our work. The analysis of these studies allowed us to identify 30 conversational agents that claim to detect depression, specifically or in combination with other disorders such as anxiety or stress disorders. As a general approach, screening was implemented in the conversational agents taking as a reference standardized or psychometrically validated clinical tests, which were also utilized as a golden standard for their validation. The implementation of questionnaires such as Patient Health Questionnaire or the Beck Depression Inventory, which are used in 65% of the articles analyzed, stand out. Conclusions: The usefulness of intelligent conversational agents allows screening to be administered to different types of profiles, such as patients (33% of relevant proposals) and caregivers (11%), although in many cases a target profile is not clearly of (66% of solutions analyzed). This study found 30 standalone conversational agents, but some proposals were explored that combine several approaches for a more enriching data acquisition. The interaction implemented in most relevant conversational agents is textbased, although the evolution is clearly towards voice integration, which in turns enhances their psychometric characteristics, as voice interaction is perceived as more natural and less invasive.Agencia Estatal de Investigación | Ref. PID2020-115137RB-I0

“I Don’t Want to Become a Number’’: Examining Different Stakeholder Perspectives on a Video-Based Monitoring System for Senior Care with Inherent Privacy Protection (by Design)

Active and Assisted Living (AAL) technologies aim to enhance the quality of life of older adults and promote successful aging. While video-based AAL solutions offer rich capabilities for better healthcare management in older age, they pose significant privacy risks. To mitigate the risks, we developed a video-based monitoring system that incorporates different privacy-preserving filters. We deployed the system in one assistive technology center and conducted a qualitative study with older adults and other stakeholders involved in care provision. Our study demonstrates diverse users’ perceptions and experiences with video-monitoring technology and offers valuable insights for the system’s further development. The findings unpack the privacy-versus-safety trade-off inherent in video-based technologies and discuss how the privacy-preserving mechanisms within the system mitigate privacy-related concerns. The study also identifies varying stakeholder perspectives towards the system in general and highlights potential avenues for developing video-based monitoring technologies in the AAL context.This work was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 861091 for the visuAAL project. This publication is based upon work from COST Action GoodBrother—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (CA19121), supported by COST (European Cooperation in Science and Technology)