论文标题
Perla：数字生态系统中抑郁症筛查的对话代理。设计，实施和验证
Perla: A Conversational Agent for Depression Screening in Digital Ecosystems. Design, Implementation and Validation
论文作者
论文摘要
大多数抑郁症评估工具基于自我报告问卷，例如患者健康问卷（PHQ-9）。这些心理测量器可以通过电子形式轻松地适应在线环境。但是，这种方法缺乏现代数字环境的相互作用和引人入胜的特征。为了使抑郁症筛查更加可用，有吸引力和有效，我们开发了Perla，这是一种能够基于PHQ-9进行访谈的对话剂。我们还进行了一项验证研究，在其中比较了传统自我报告问卷与佩拉的自动访谈所获得的结果。分析这项研究的结果，我们得出了两个重要的结论：首先，Perla受到互联网用户的最爱，比传统的基于表格的问卷提出了2.5倍以上；其次，她的心理测量特性（Cronbach的α为0.81，敏感性为96％，特异性为90％）非常出色，并且与传统的良好抑郁症筛查问卷相当。
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.
