Automatic topic selection for long-term interaction with embodied conversational agents in health coaching: A micro-randomized trial

Psychosomatic mechanisms of heart failure symptoms on quality of life in patients with chronic heart failure: A multi-centre cross-sectional study

AIMS

To determine the contributions of different kinds of symptoms to the quality of life and mediating effect of psychological and physical symptoms between heart failure symptoms and quality of life.

DESIGN

A multi-centre cross-sectional study.

METHODS

2006 chronic heart failure patients from four cities were recruited in China from January 2021 to December 2022. Patients' symptoms and quality of life were self-reported, and data were analysed using correlation analysis, dominance analysis and mediating effects analysis.

RESULTS

The dominance analysis revealed that the overall mean contributions of heart failure, psychological and physical symptoms were .083, .085 and .111; 29.5%, 30.2% and 39.5% of the known variance. And heart failure symptoms could negatively affect quality of life through psychological and physical symptoms, accounting for 28.39% and 22.95% of the total effect. Heart failure symptoms could also affect quality of life through the chain-mediated effect of physical and psychological symptoms, accounting for 16.74%.

CONCLUSIONS

Physiological symptoms had the strongest effect on quality of life and heart failure symptoms had the weakest. Most of the effect for heart failure symptoms on quality of life in chronic heart failure patients was mediated by psychological and physiological symptoms.

RELEVANCE TO CLINICAL PRACTICE

It is important to design non-pharmacological intervention plans for the enhancement of physical and psychological symptoms' management skills, to reduce the adverse impact of heart failure symptoms on quality of life.

REPORTING METHOD

Study methods and results reported in adherence to the STROBE checklist.

NO PATIENT OR PUBLIC CONTRIBUTION

No patients or members of the public were involved in the study.

Unlocking human-like conversations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents

BACKGROUND

Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes.

OBJECTIVES

To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions.

METHODOLOGY

A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria.

RESULTS

Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing.

CONCLUSIONS

The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.