ArticleFrontiers in psychiatry2025
Persuasive chatbot-based interventions for depression: a list of recommendations for improving reporting standards.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Background: Depression is the leading cause of disability worldwide. Digital interventions based on chatbots could be an alternative or complementary approach to the treatment of depression. However, the absence of technical information in papers on depression-related chatbots often obstructs study reproducibility and hampers evaluating intervention efficacy. Objective: This study aims to identify specific characteristics of chatbots for depression and formulate recommendations for improving reporting standards. Methods: In an initial step, a list of items that must be reported was defined based on a previous review on digital interventions for depression, the Behavior Change Wheel framework, and a taxonomy for defining archetypes of chatbots. To capture the existing knowledge on the development of chatbots for depression, a literature review was conducted in a second step. From the identified studies, we tried to extract information related to the items from our initial list and described in this way the chatbots and their evaluation. As a third step, the findings of the literature review were analyzed, leading to an agreement on a list of recommendations for reporting chatbot-based interventions for depression. Results: The items of the recommendation list for reporting fall into four dimensions: General information; Chatbot-based depression intervention functions; Technical data; and Study. Through a literature review, a total of 23 studies on chatbots for depression were identified. We found that a lot of information as requested by our initial reporting list was missing, specifically regarding the involvement of natural language processing, data privacy handling, data exchange with third-party providers, and hosting. Additionally, technical evaluation details were often unreported in many papers. Conclusion: Studies on chatbots for depression can improve reporting by specifically adding more technical details and chatbot evaluation. Such reporting of technical details is important even in papers on clinical trials that utilize chatbots in order to allow reproducibility and advance this field. Future work could obtain expert consensus on the recommended reporting items for chatbot-based interventions for depression.
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