Perla: A Conversational Agent for Depression Screening in Digital Ecosystems. Design, Implementation and Validation
This work addresses the need for more engaging and accessible depression screening tools for internet users, representing an incremental improvement over existing digital forms.
The researchers tackled the problem of low engagement in online depression screening by developing Perla, a conversational agent based on the PHQ-9 questionnaire, which achieved over 2.5 times more reach than traditional forms and demonstrated excellent psychometric properties with a Cronbach's alpha of 0.81, sensitivity of 96%, and specificity of 90%.
Most depression assessment tools are based on self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9). These psychometric instruments can be easily adapted to an online setting by means of electronic forms. However, this approach lacks the interacting and engaging features of modern digital environments. With the aim of making depression screening more available, attractive and effective, we developed Perla, a conversational agent able to perform an interview based on the PHQ-9. We also conducted a validation study in which we compared the results obtained by the traditional self-report questionnaire with Perla's automated interview. Analyzing the results from this study we draw two significant conclusions: firstly, Perla is much preferred by Internet users, achieving more than 2.5 times more reach than a traditional form-based questionnaire; secondly, her psychometric properties (Cronbach's alpha of 0.81, sensitivity of 96% and specificity of 90%) are excellent and comparable to the traditional well-established depression screening questionnaires.