BALMS is a benchmark for evaluating large language model (LLM) agents that analyze longitudinal wearable data to predict mental‑health wellbeing scores and generate evidence‑grounded rationales. It covers three real‑world datasets, two task families (score prediction and rationale generation), and tests five LLM backbones across open‑ and closed‑source paradigms. The study finds that zero‑shot agents rarely beat a simple mean baseline, and while chain‑of‑thought prompting helps reasoning, it does not ensure temporal grounding or numerical accuracy.
By Yu Yvonne Wu, Arvind Pillai, Yuliang Chen, Yuwei Zhang, Sudarshan Regmi, Tess Z. Griffin, Michael V. Heinz, Lisa A. Marsch, Nicholas C. Jacobson, Andrew Campbell
The study introduces a clinician‑in‑the‑loop benchmark to assess whether large language models can generate evidence‑grounded Brief Hierarchical Taxonomy of Psychopathology (B‑HiTOP) item profiles from multimodal data, including passive sensing, ecological momentary assessment, and questionnaires. Using the GLOBEM dataset, the authors create 14,592 participant‑day instances aligned to 29 B‑HiTOP items across five spectra, and evaluate evidence compatibility rather than diagnostic accuracy. Two‑stage prediction improves compatibility for EMA and questionnaire evidence but reduces it for passive sensing and combined evidence, yielding more conservative score distributions across models, spectra, and evidence settings.
By Xiyun Hu, Xiangyuan Xue, Yuting Lyu, Hanya Shao, Jingping Nie
arXiv:2608. 07518v1 Announce Type: cross Abstract: Longitudinal, in-the-wild, wearable sensing yields day-level physiology, sleep, activity, and environmental streams, whereas affect and cognition are labeled only episodically (per waves).
By Igor Matias, Maximilian Haas, Eric J. Daza, Matthias Kliegel, Katarzyna Wac
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
By Esther Brown, Karis Moon, Victoria Dean, Finale Doshi-Velez
arXiv:2606. 18596v1 Announce Type: cross Abstract: Sleep diaries are central to behavioral sleep medicine and cognitive behavioral therapy for insomnia, yet daily completion is difficult to sustain, and static forms often provide limited context for interpreting night-to-night sleep variation.
By Amama Mahmood, Bokyung Kim, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Michael T. Smith, Chien-Ming Huang
The paper introduces a Concept-Integrated Transformer (CIT) that uses a pretrained large language model to generate concept abnormality targets with confidence weights, eliminating the need for manual concept annotation. CIT is applied to mobile sensing data from two longitudinal datasets, achieving the highest F1 score on the AFFECT dataset (0.756) and tying for the highest on a PHQ-9 dataset (0.765). The model’s learned concept scores reveal interpretable behavioral and physiological patterns, such as differences in sleep quantity and quality between high and low negative affect groups.
By Yuning Wang, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg