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Persuasive chatbot-based interventions for depression: a list of recommendations for improving reporting standards
Kerstin Denecke1, Octavio Rivera Romero2,3, Rolf Wynn4,5
1AI for Health, Institute Patient-centered Digital Health, Department Engineering and Computer Sciences, Bern University of Applied Sciences, Biel, Switzerland.
Frontiers in Psychiatry
|July 4, 2025
Summary
Reporting standards for depression chatbots need improvement. This study identified missing technical details in current research, emphasizing the need for comprehensive reporting to enhance reproducibility and efficacy of digital mental health interventions.
Area of Science:
- Digital Health
- Mental Health Technology
- Computational Psychiatry
Background:
- Depression is a leading global cause of disability.
- Chatbots offer a potential digital intervention for depression treatment.
- Current research on depression chatbots lacks technical details, hindering reproducibility and efficacy evaluation.
Purpose of the Study:
- To identify key characteristics of depression chatbots.
- To establish recommendations for improving reporting standards in this field.
Main Methods:
- Defined essential reporting items using prior reviews and frameworks.
- Conducted a literature review to extract information on existing depression chatbots.
- Analyzed findings to develop reporting recommendations.
Main Results:
- Identified 23 studies on chatbots for depression.
- Found significant omissions in reporting, particularly concerning natural language processing, data privacy, third-party data exchange, and hosting.
- Technical evaluation details were frequently unreported.
Conclusions:
- Enhanced reporting of technical details and evaluation is crucial for depression chatbot studies.
- Improved reporting standards will advance the field and ensure reproducibility of digital mental health interventions.
- Future work should focus on achieving expert consensus on reporting recommendations.
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