Related Experiment Video
Updated: Jan 14, 2026

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
Published on: July 7, 2023
Tweaking the Messages and Approaching the Glass Box: Using AI Chatbots to Promote Help-Seeking for Depressive
Jingyuan Shi1, Kun Xu2, Xiaobei Chen2
1Department of Interactive Media, Hong Kong Baptist University, Hong Kong SAR, China.
AI health chatbots are more trusted when they explain high human involvement in their algorithms, improving attitudes toward seeking help. Message targeting and human knowledge involvement also influence user reactions and help-seeking behaviors.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence in Healthcare
- Digital Health Communication
Background:
- AI-based health chatbots are increasingly used for health promotion.
- Understanding user trust and attitudes towards AI recommendations is crucial for effective health interventions.
- The role of human involvement in AI algorithms and message framing requires further investigation.
Purpose of the Study:
- To examine how message framing and explanations of human knowledge involvement in AI algorithms affect users' trust and attitudes towards chatbot recommendations.
- To investigate the influence of message targeting, human knowledge involvement, and user depression tendency on psychological reactance and subsequent help-seeking behaviors.
- To contribute to the understanding of AI's persuasive mechanisms in health promotion and its application in mental health.
Main Methods:
- An online experiment was conducted with 374 participants.
- A two-level human-machine communication framework was employed.
- Independent variables included chatbot message framing, explanations of human knowledge involvement, message targeting, and user depression tendency.
Main Results:
- Explanations of high human knowledge involvement in AI algorithms significantly increased user trust and positive attitudes toward help-seeking.
- Message targeting, human knowledge involvement, and user depression tendency jointly influenced psychological reactance.
- Psychological reactance mediated the effect of these factors on attitudes toward seeking help from social networks.
Conclusions:
- Transparency regarding human involvement in AI algorithms can enhance user trust in health chatbots.
- AI-driven health promotion messages need careful design considering message targeting and user characteristics to avoid negative psychological reactance.
- Findings offer insights for optimizing AI applications in mental health promotion and persuasive health communication.
Related Concept Videos
Depression: Overview
Long-term Depression
Long-term Depression
Calcium Ion Concentration Mechanism
If over...
Depressive Disorders: MDD and Dysthymia
Self-Help Support Groups
Accessibility and Cost-Effectiveness
One of the primary strengths of self-help...
Depressive Disorders: Etiology
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...

