LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models

• URL: http://arxiv.org/abs/2512.23025v1
• Date: Sun, 28 Dec 2025 18:00:57 GMT
• Title: LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models
• Authors: Wenxuan Xu, Arvind Pillai, Subigya Nepal, Amanda C Collins, Daniel M Mackin, Michael V Heinz, Tess Z Griffin, Nicholas C Jacobson, Andrew Campbell,
• Abstract summary: We introduce LENS, a framework that aligns multimodal sensing data with language models to generate mental-health narratives.<n>LENS constructs a large-scale dataset by transforming responses related to depression and anxiety symptoms into natural-language descriptions.<n>Our results show that LENS outperforms strong baselines on standard NLP metrics and task-specific measures of symptom-severity accuracy.
• Score: 5.041844772782674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challenging. Current LLMs cannot natively ingest long-duration sensor streams, and paired sensor-text datasets are scarce. To address these challenges, we introduce LENS, a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. LENS first constructs a large-scale dataset by transforming Ecological Momentary Assessment (EMA) responses related to depression and anxiety symptoms into natural-language descriptions, yielding over 100,000 sensor-text QA pairs from 258 participants. To enable native time-series integration, we train a patch-level encoder that projects raw sensor signals directly into an LLM's representation space. Our results show that LENS outperforms strong baselines on standard NLP metrics and task-specific measures of symptom-severity accuracy. A user study with 13 mental-health professionals further indicates that LENS-produced narratives are comprehensive and clinically meaningful. Ultimately, our approach advances LLMs as interfaces for health sensing, providing a scalable path toward models that can reason over raw behavioral signals and support downstream clinical decision-making.

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