Factors Associated With the Level of Trust in Health Information Robots Among the General Population From a Socioecological Model Perspective: Network Analysis

Area of Science:

• Robotics in Healthcare
• Artificial Intelligence (AI)
• Health Communication

Background:

• Robots are increasingly used for health information delivery.
• Trust in AI-driven health information remains a significant challenge for individuals.
• Various socio-ecological factors influence trust in health information robots.

Purpose of the Study:

• To investigate factors associated with trust in health information robots in China.
• To apply a socio-ecological model perspective to understand trust.
• To identify central trust indicators using network analysis.

Main Methods:

• Nationwide survey of 30,054 participants in China (June-August 2023).
• Self-developed questionnaire to measure trust in health information robots.
• Generalized linear models and network analysis to identify influencing factors.

Main Results:

• Positive associations with trust: chronic disease diagnosis, agreeableness, openness, higher education, better self-rated health, health literacy, perceived social support, and higher income.
• Negative associations with trust: older age, neuroticism, and urban living.
• Central indicators: family health and perceived social support; agreeableness and education were upstream influencers.

Conclusions:

• Trust in health information robots is multifaceted, influenced by individual, social, and demographic factors.
• Considering identified trust factors can enhance AI healthcare acceptance and adherence.
• Findings contribute to the development and application of AI in healthcare IT.