The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change.

Importance: The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion.

Objectives: The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically.

Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic.

Breast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effective method for early detection, screening rate has remained low. Given that understanding health perception is critical to understand health decisions, our study utilized the Health Belief Model-based deep learning method to predict and examine public health beliefs in breast cancer and its screening behavior.

Mental Health Pandemic During the COVID-19 Outbreak: Social Media As a Window to Public Mental Health.

Intensified preventive measures during the COVID-19 pandemic, such as lockdown and social distancing, heavily increased the perception of social isolation (i.e., a discrepancy between one's social needs and the provisions of the social environment) among young adults.