Post-intensive care syndrome (PICS) is a multifaceted condition that arises from prolonged stays in an intensive care unit (ICU). While preventing PICS among ICU patients is becoming increasingly important, interventions remain limited. Building on evidence supporting the effectiveness of art exposure in addressing the psychological aspects of PICS, we propose a novel art therapy solution through a collaborative Human-AI approach that enhances personalized therapeutic interventions using state-of-the-art Visual Art Recommendation Systems. We developed two Human-in-the-Loop (HITL) personalization methods and assessed their impact through a large-scale user study (N = 150). Our findings demonstrate that this Human-AI collaboration not only enhances the personalization and effectiveness of art therapy but also supports therapists by streamlining their workload. While our study centres on PICS intervention, the results suggest that human-AI collaborative Art therapy could potentially benefit other areas where emotional support is critical, such as cases of anxiety and depression.

Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.

Nature experiences, especially the visual aspects of nature, have been widely used to facilitate relaxation. Fueled by digital technology, simulated visual nature experiences have gained popularity in creating healing environments that induce relaxation. However, while easily applicable, not all nature-imitating visuals lead to relaxation. How to effectively design relaxing visual nature experiences remains largely unexplored. This paper investigates how different nature qualities facilitate relaxing visual experiences and the roles of two personal characteristics (mood and nature-relatedness) play. Through an online survey and interviews, we assessed 16 nature video clips, representing eight distinctive nature qualities, and compared perceived experiences while considering the influence of personal characteristics. The results indicate four types of visual qualities (engaging, instinctive, ambient, and derivative) underlying nature-induced relaxation, and show that nature relatedness influences the degree to which nature video clips elicit relaxation. We discuss design implications for creating personalized digital nature.