The impact of positive attitude towards artificial intelligence and multiple mediation functions of satisfying basic...

With the wide application of Artificial Intelligence (AI) in the health field, this study aims to explore the intrinsic connections among AI, individual psychology, and digital health, and reveal the multiple mediating roles of positive attitudes toward AI and the satisfaction of basic psychological needs in this process. The study adopts a design combining cross-sectional questionnaire survey and short-term controlled experiment: It collects data from 400 adults who use intelligent health monitoring devices or AI-assisted diagnosis systems via questionnaire survey. Meanwhile, the study selects 200 of these adults with experience in using intelligent monitoring devices. A controlled experiment is conducted with 100 people in the experimental group and 100 people in the control group. The information collected includes respondents' attitudes toward AI. Satisfaction with basic psychological needs (autonomy, competence, and relatedness) is also gathered. Additionally, the digital health status is collected, which includes two dimensions: physical and psychological. The study results show that: AI functional characteristics (accuracy, ease of use, interpretability, with all correlation coefficients being significant) and contact frequency are significantly positively correlated with positive attitudes toward AI. While the impact of AI type is not significant. Positive attitudes toward AI are extremely significantly positively correlated with the satisfaction of autonomy (r = 0.52), competence (r = 0.55), and relatedness (r = 0.50) (p < 0.001). The satisfaction of basic psychological needs is significantly positively correlated with digital health (physical health and mental health). At the theoretical level, this study constructs and verifies the integrated model of "AI-related factors-positive attitudes-satisfaction of basic psychological needs-digital health", improves the theory of micro-psychological mechanisms in the field of AI and digital health, and supplements empirical evidence for multiple mediating effects. At the practical level, the study results provide specific guidance for the functional optimization of AI health products (such as improving accuracy and ease of use) and the personalized design of digital health services, which helps enhance users' acceptance and usage efficiency of AI digital health products. It should be noted that the proportion of the elderly group (aged 65 and above) in the questionnaire sample of this study is only 5% (20 people), and the study data are mainly cross-sectional. This may limit the generalizability of the results to the elderly group and the inferential power of causal relationships. In the future, it is necessary to further verify the model stability by expanding the sample structure and conducting longitudinal studies.