arXiv AI By Amama Mahmood, Bokyung Kim, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Michael T. Smith, Chien-Ming Huang

Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 21

An Agentic Just-in-Time Adaptive Intervention System for Personalized Sleep Support: Proof-of-Concept Study with N of 1 Data

The study presents a proof‑of‑concept just‑in‑time adaptive intervention (JITAI) for sleep support that employs an AI agent to analyze 30 days of personal sleep and behavioral data, such as physical activity, smartphone use, and bedtime routines. The agent, running on Home Assistant, reviews the data, evaluates existing reminders, adapts interventions, and records decisions for human review, while limiting reminders to no more than three per day. Initial runs confirmed technical feasibility, successfully completing data review and intervention decisions and saving decision records for future analysis.

By Nick Rezaee, Chelsea Boccagno
arXiv Computation and Language
Aug 28

BALMS: Benchmarking Agentic LLMs for Longitudinal Mental Health Sensing

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
arXiv Computation and Language
Sep 22

Toward Personalized Sleep Guidance from Wearable Data Using Language Models

arXiv:2609.22463v1 Announce Type: new Abstract: Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering r...

By Yusheng Tan, Running Zhao, Sofia Angel, Ninghui Hao, Ash Arian, Nikita N. Dulin, Jay Lin, Ou Zhu, Faiza Shaik, Xinxing Yang, Bonnie W. Leung, Katie Roster, Arlene Ruiz de Luzuriaga, Kenneth Lee, Alejandra Lastra, Habibul Ahsan, Guihong Wan
arXiv AI
Aug 10

Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases

arXiv:2608. 06380v1 Announce Type: cross Abstract: Fatigue, sleep, or disturbances in daily activities are common symptoms among patients with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID).

By Julian Fierrez, Alejandro Pe\~na, Aythami Morales, Ruben Tolosana, Ruben Vera-Rodriguez, Meenakshi Chatterjee, Ahmaniemi Teemu, Wan-Fai Ng, Walter Maetzler, Nikolay V. Manyakov, Jennifer Kudelka, Ralf Reilmann, C. Janneke van der Woude, Kristen Davies, Victoria Macrae, IDEA-FAST Consortium