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 examined whether ultra‑brief naturalistic concern text could complement wearable sensor data for monitoring student health. Over a year, 458 university students provided three‑word responses to a bi‑monthly prompt while wearing Oura rings, and the researchers compared different NLP methods to link these brief texts to nine sleep and physical activity outcomes. Findings indicated that weeks with academic‑concern framing correlated with lower physical activity, while emotional exhaustion language correlated with poorer sleep quality and lower heart rate variability, suggesting that affective content may add value to passive physiological monitoring.
By Tamunotonye Harry, Johanna Hidalgo, Matthew Price, Yuanyuan Feng, Kathryn Stanton, Connie Tompkins, Peter Sheridan Dodds, Mikaela Irene Fudolig, Laura Bloomfield, Christopher Danforth
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
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
By Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali, Franca Delmastro
arXiv:2607. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.
By Shiyu Teng, Haichen Yu, Jiaqing Liu, Hao Sun, Yu Song, Shurong Chai, Ruibo Hou, Lanfen Lin, Yen-Wei Chen
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