Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study
Background: There are far more patients in mental distress than there is time available for mental health professionals to support them. Although digital tools may help mitigate this issue, critics have suggested that technological solutions that lack human empathy will prevent a bond or therapeutic alliance from being formed, thereby narrowing these solutions’ efficacy. Objective: We aimed to investigate whether users of a cognitive behavioral therapy (CBT)–based conversational agent would report therapeutic bond levels that are similar to those in literature about other CBT modalities, including face-to-face therapy, group CBT, and other digital interventions that do not use a conversational agent. Methods: A cross-sectional, retrospective study design was used to analyze aggregate, deidentified data from adult users who self-referred to a CBT-based, fully automated conversational agent (Woebot) between November 2019 and August 2020. Working alliance was measured with the Working Alliance Inventory-Short Revised (WAI-SR), and depression symptom status was assessed by using the 2-item Patient Health Questionnaire (PHQ-2). All measures were administered by the conversational agent in the mobile app.
Introduction. Significant barriers to mental health care are persistent [1]. The increased burden of depression and anxiety, which arose during the COVID-19 pandemic, has exacerbated this issue [2], as the
Discussion / Conclusion. This is the first study of working alliance among users of a CA for mental health. Most users were female (20,734/36,070, 57.48%) and had PHQ-2 scores that were indicative of depression (19,719/36,070, 54.67%). Working alliance scores were comparable to those in previously published studies on traditional, human-delivered services across different treatment modalities. Working alliance scores were highest for the bond subscale, suggesting that this subscale is a viable construct for CAs and should be included in hypothesized frameworks of digital working alliance.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
How do chatbots affect human self-disclosure and emotional engagement?- How does emotional dependence on chatbots affect user wellbeing?
- Can people form genuine bonds with partners they know are not human?
- How does consciousness attribution drive emotional dependence on chatbots?
- What harms might chatbots cause through stigma expression and delusion reinforcement?
- Can people form therapeutic bonds with tools they know are not human?
- What clinical harms might hide behind positive therapeutic bond measurements?
- Can therapeutic bonds exist without genuine reciprocity or mutual understanding?
- Why might patients feel closest to therapists when misalignment is highest?
- Can working alliance be measured in real time during therapy sessions?
- Why do therapists and patients report misaligned perceptions of the working relationship?
- Can real-time therapist feedback improve outcomes using computational alliance measurement?
- Can single-turn empathy advantage predict multi-turn therapeutic outcomes?
- What separates generating empathic responses from maintaining therapeutic alliance?
- How does turn-level working alliance inference enable real-time therapist feedback?
- Does true understanding matter for therapeutic benefits of disclosure?
- Should chatbots be designed as therapist support tools rather than replacements?
- How do language models interpolate user feelings in therapeutic contexts?
- How should AI systems separate feeling interpretation from objective therapeutic guidance?
- Why do mental health chatbots fail at synchrony despite strong language models?
- Do therapeutic chatbots adequately detect crisis situations and safety risks?
- How do dropout rates and low adherence affect chatbot therapy outcomes?