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. Little is known about its capabilities to identify motivation states and provide appropriate information to facilitate behavior change progression. MATERIALS AND METHODS: We evaluated 3 GAs, ChatGPT, Google Bard, and Llama 2 in identifying motivation states following the TTM stages of change. GAs were evaluated using 25 validated scenarios with 5 health topics across 5 TTM stages. The relevance and completeness of the responses to cover the TTM processes to proceed to the next stage of change were assessed. RESULTS: 3 GAs identified the motivation states in the preparation stage providing sufficient information to proceed to the action stage. The responses to the motivation states in the action and maintenance stages were good enough covering partial processes for individuals to initiate and maintain their changes in behavior. However, the GAs were not able to identify users' motivation states in the precontemplation and contemplation stages providing irrelevant information, covering about 20%-30% of the processes. DISCUSSION: GAs are able to identify users' motivation states and provide relevant information when individuals have established goals and commitments to take and maintain an action. However, individuals who are hesitant or ambivalent about behavior change are unlikely to receive sufficient and relevant guidance to proceed to the next stage of change. CONCLUSION: The current GAs effectively identify motivation states of individuals with established goals but may lack support for those ambivalent towards behavior change.

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. Social isolation is closely associated with situational loneliness (i.e., loneliness emerging from environmental change), a risk factor for depressive symptoms. Prior research suggested vulnerable young adults are likely to seek support from an online social platform such as Reddit, a perceived comfortable environment for lonely individuals to seek mental health help through anonymous communication with a broad social network. Therefore, this study aims to identify and analyze depression-related dialogues on loneliness subreddits during the COVID-19 outbreak, with the implications on depression-related infoveillance during the pandemic. Our study utilized logistic regression and topic modeling to classify and examine depression-related discussions on loneliness subreddits before and during the pandemic. Our results showed significant increases in the volume of depression-related discussions (i.e., topics related to mental health, social interaction, family, and emotion) where challenges were reported during the pandemic. We also found a switch in dominant topics emerging from depression-related discussions on loneliness subreddits, from dating (prepandemic) to online interaction and community (pandemic), suggesting the increased expressions or need of online social support during the pandemic. The current findings suggest the potential of social media to serve as a window for monitoring public mental health. Our future study will clinically validate the current approach, which has implications for designing a surveillance system during the crisis.

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. The results showed that the trends in public health perception are sensitive to political (i.e., changes in health policy), sociological (i.e., representation of disease and its preventive care by public figure or organization), psychological (i.e., social support), and environmental factors (i.e., COVID-19 pandemic). Our study explores the roles social media can play in public health surveillance and in public health promotion of preventive care.