Digital Therapeutic Alliance

The therapeutic alliance (TA), also referred to as the working alliance, describes the collaborative relationship between a therapist and a client in face-to-face therapy. It involves trust, mutual understanding, and cooperation in working toward therapeutic goals. Similarly, Bordin (1979) conceptualized the therapeutic alliance as consisting of three key components: agreement on goals, agreement on tasks, and the emotional bond between therapist and client. This is consistently linked to better treatment outcomes across different mental health conditions and therapy types (Flückiger et al., 2018; Karver et al., 2018).

In recent years, digital mental health tools such as mobile apps and AI-based chatbots have become increasingly common (Lam et al., 2024). Many are based on evidence-based approaches like CBT and mindfulness (Baños et al., 2022). While early interventions showed low engagement, personalization and supportive features have improved outcomes, with chatbots showing promising effects on depression and anxiety symptoms.

As these tools continue to develop and show potential benefits, researchers have begun to explore not only their effectiveness but also the relationships users form with them.

Given the importance of therapeutic alliance in face-to-face therapy, researchers have begun examining whether a similar relationship can develop between users and digital mental health interventions. This emerging concept is referred to as digital therapeutic alliance (DTA) and describes the relational bond formed between individuals and automated mental health tools. Early research suggests that users can indeed experience a sense of connection with mental health applications, supporting the existence of DTA (Berry et al., 2018; Tong et al., 2023).

However, traditional models of therapeutic alliance were developed for human therapist–client relationships and may not fully reflect the unique characteristics of digital interactions. For example, flexibility in how and when users engage with an app has been identified as a particularly important element in digital therapeutic relationships, yet it is not typically emphasized in face-to-face therapy frameworks (Tong et al., 2022).

To better understand and measure this digital form of alliance, Tong et al. (2023) conducted interviews with users of mental health applications to identify key components of DTA. Five core elements emerged: flexible app use, active user involvement, emotional responses to the app, comfort in sharing personal information, and working toward clear goals. These findings informed the development of the Melbourne-Manchester Digital Therapeutic Alliance Scale (MM-DTA), a 39-item instrument specifically designed for fully automated mental health tools (Tong et al., 2025).

The MM-DTA was tested across both mindfulness-based and CBT-based applications and revealed four main dimensions: flexibility, emotional connection, shared goals, and openness. The scale demonstrated strong reliability and was positively associated with user engagement and mental health outcomes, suggesting it is a useful tool for assessing digital therapeutic alliance (Tong et al., 2025).

These findings indicate that digital therapeutic alliance involves both relational and functional components, combining emotional connection with collaborative, goal-oriented processes. The inclusion of flexibility further highlights a feature that appears particularly important in digital contexts, reflecting how users value autonomy in their interactions with mental health tools (Tong et al., 2022; Tong et al., 2025).

To conclude, the findings highlight that meaningful therapeutic relationships can develop between users and automated mental health tools, and that digital therapeutic alliance plays an important role in engagement and mental health outcomes.

References:

Baños, R. M., Herrero, R., & Vara, M. D. (2022). What is the current and future status of digital mental health interventions? The Spanish Journal of Psychology, 25, e5.

Berry, K., Salter, A., Morris, R., James, S., & Bucci, S. (2018). Assessing therapeutic alliance in the context of mHealth interventions for mental health problems: Development of the mobile Agnew Relationship Measure (mARM) questionnaire. Journal of Medical Internet Research, 20(4), e8252.

Bordin, E. S. (1979). The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research & Practice, 16(3), 252.

Flückiger, C., Del Re, A. C., Wampold, B. E., & Horvath, A. O. (2018). The alliance in adult psychotherapy: A meta-analytic synthesis. Psychotherapy, 55(4), 316.

Karver, M. S., De Nadai, A. S., Monahan, M., & Shirk, S. R. (2018). Meta-analysis of the prospective relation between alliance and outcome in child and adolescent psychotherapy. Psychotherapy, 55(4), 341

Lam, R. W., Kennedy, S. H., Adams, C., Bahji, A., Beaulieu, S., Bhat, V., ... & Milev, R. V. (2024). Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 update on clinical guidelines for management of major depressive disorder in adults. The Canadian Journal of Psychiatry, 69(9), 641–687.

Tong, F., Lederman, R., D'Alfonso, S., Berry, K., & Bucci, S. (2022). Digital therapeutic alliance with fully automated mental health smartphone apps: A narrative review. Frontiers in Psychiatry, 13, 819623.

Tong, F., Lederman, R., D'Alfonso, S., Berry, K., & Bucci, S. (2023). Conceptualizing the digital therapeutic alliance in the context of fully automated mental health apps: A thematic analysis. Clinical Psychology & Psychotherapy, 30(5), 998–1012.

Tong, F., Lederman, R., D’Alfonso, S., Berry, K., & Bucci, S. (2025). Development of a digital therapeutic alliance scale (MM-DTA) in the context of fully automated mental health apps. Behaviour & Information Technology, 44(17), 4286–4300. https://doi.org/10.1080/0144929X.2025.2469672