P ro M ind- LLM : Proactive Mental Health Care via Causal Reasoning with Sensor Data (2025)

Abstract: Mental health risk is a critical global public health challenge, necessitating innovative and reliable assessment methods. With the development of large language models (LLMs), they stand out to be a promising tool for explainable mental health care applications. Nevertheless, existing approaches predominantly rely on subjective textual mental records, which can be distorted by inherent mental uncertainties, leading to inconsistent and unreliable predictions. To address these limitations, this paper introduces ProMind-LLM. We investigate an innovative approach integrating objective behavior data as complementary information alongside subjective mental records for robust mental health risk assessment. Specifically, ProMind-LLM incorporates a comprehensive pipeline that includes domain-specific pretraining to tailor the LLM for mental health contexts, a self-refine mechanism to optimize the processing of numerical behavioral data, and causal chain-of-thought reasoning to enhance the reliability and interpretability of its predictions. Evaluations of two real-world datasets, PMData and Globem, demonstrate the effectiveness of our proposed methods, achieving substantial improvements over general LLMs. We anticipate that ProMind-LLM will pave the way for more dependable, interpretable, and scalable mental health case solutions.

## introduction in fact, mental health disorders have the highest prevalence rates compared to many other major health conditions. according to who, approximately 25% of people globally experience a mental health issue @xcite . moreover, the covid-19 pandemic and other global crises have exacerbated this issue, leading to a 25.6% increase in anxiety and a 27.6% rise in depression since 2020 @xcite . the widespread nature of mental disorders underscores the need for developing automated detection tools to tackle this public health challenge @xcite . currently, large language models (llms) have demonstrated remarkable success across a variety of domains @xcite @xcite . leveraging their advanced generalization and reasoning abilities, researchers have demonstrated their potential applications in mental health care @xcite @xcite . llms can analyze user-generated data that reflects mental states, extracting contextual information to uncover subtle indicators of mental health disorders @xcite . for instance, prior works approved the effectiveness of using social media posts @xcite or conversational records @xcite to assess the presence of mental health issues. despite these promising results, these approaches are limited by their exclusive reliance on subjective textual data. such data can be influenced by transient factors, including the user's current mood or willingness to disclose accurate information, which undermines the reliability of predictions @xcite . to address this limitation, incorporating objective factors, such as behavioral sensor data (e.g.heart rate, sleep quality), offers a promising avenue to complement subjective data and enhance the accuracy of mental health predictions @xcite . recent studies have integrated other modalities such as speech @xcite and video @xcite for mental disorder detection and achieved improvements compared to using subjective data only. nevertheless, the combined use of subjective records and objective behavior data for llm-based mental health assessment remains largely underexplored. developing an llm approach that effectively combines objective behavior data with subjective mental records poses several challenges. first, given the potential uncertainties in subjective mental records, llms must enhance their capacity to navigate the variability embedded within such data effectively. second, most behavior data, such as heart rate, sleep quality, and exercise readings, are represented as lengthy numerical sequences @xcite . although these sequences can be formatted as text for llm inputs, their sheer length and numeric nature can impede the model's ability for accurate interpretation @xcite . third, enabling the llm to fully utilize multimodal data, especially the causal relations @xcite between objective behavior data and subjective mental records, to further improve the outcome robustness requires a specific design. to address these issues, we introduce promind-llm, which builds upon the causal relations among mental status, mental records, and behavior data (figure . 1), to deliver robust mental health risk assessment. for the first challenge (sec. 3.1), we construct a comprehensive mental health corpus and adopt continuous pertaining (pt) @xcite to enhance the base model's understanding of mental health concepts, a widely used strategy in the development of prior mental health llms @xcite . following pt, we implement counterfactual learning-based supervised finetuning (sft), which generates misleading information in users' mental records to challenge and improve the llm's resilience against uncertainties in subjective data. promind-llm addresses the second issue (sec. 3.2) by proposing a self-refine mechanism to format the behavior data, allowing the llm to iteratively update and optimize the format based on its own feedback. this approach not only shortens behavior inputs while preserving key features relevant to mental status but also enhances the model's ability to interpret behavior insights. for the third challenge (sec. 3.3), promind-llm initially employs chain-of-thought (cot) @xcite reasoning, integrating the causal relations between mental status and behavior. this allows the model to analyze the mental records and behavior data individually and account for their mutual influence, rendering a more robust mental risk prediction. additionally, promind-llm then utilizes counterfactual reasoning to further refine its judgment. by exploring "what-if" scenarios-e.g., "what if the user's eating habit were normal? would this absence change the outcome?"-the model evaluates alternative outcomes based on hypothetical changes to the input data @xcite . this process enables the llm to identify true indicators of mental risks, and ultimately confirm the outcome's reliability. together, these reasoning steps form the causal cot scheme in promind-llm. to sum up, we make the following contributions in this paper: ## methodology our proposed method, as illustrated in figure . 2, comprises three key components: domain-specific training, self-refine-based behavior data preprocessing, and causal cot reasoning. together, these components enable robust mental risk prediction by integrating objective behavior data with subjective mental records. below, we provide a detailed explanation of each design. ## conclusion this paper presents promind-llm, the first llmbased approach to integrate both objective behavioral data and subjective mental records for mental risk analysis. our pipeline includes domainspecific training to equip the base model with mental health concepts, the self-refine mechanism for behavior data preprocessing to enhance numerical data comprehension, and causal cot reasoning to deliver precise predictions by leveraging causal relations between mental records and behavioral data. the experimental results validate the effectiveness of our methods, highlighting promind-llm's ability to advance the research in this domain. we believe promind-llm marks a significant step toward achieving proactive mental health care.