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).
arXiv:2605. 17679v2 Announce Type: replace-cross Abstract: Cancer survivors face elevated rates of depression, anxiety, and emotional distress, yet self-report may be unavailable at some moments when support is relevant, a challenge we term the diary paradox.
arXiv:2606. 00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings.
WearableQA is a new benchmark that tests AI systems on health reasoning using real-world wearable data from 200 users, each with up to 500 days of daily measurements. It contains 4,084 ten‑option multiple‑choice questions derived from wearable time series, blood biomarkers, and demographics, and is organized into 16 question types that distinguish data‑driven computation from physiological interpretation and single‑signal from cross‑signal reasoning. Evaluation of 14 large language models shows wide performance gaps, indicating that the benchmark remains challenging and useful for diagnosing model capabilities.
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.
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
The paper investigates on-device language models (ODLMs) for predicting stress in a mobile health context, focusing on privacy-preserving, cloud-independent inference. Using zero‑shot prompting, the authors evaluate ODLMs across multimodal data—objective sensor features and subjective self‑reports—measuring predictive accuracy, latency, and throughput. Results indicate that sensor features slightly outperform self‑reports, and that lightweight sub‑2B models deliver low latency with predictable resource usage, underscoring both the potential and practical limits of ODLMs for mobile mental health.
arXiv:2606. 18506v1 Announce Type: new Abstract: Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue.
Commercial wearable devices continuously capture rich physiological data (e. g.
The paper introduces Sensori, a self‑supervised foundation model that learns health representations from 24‑hour raw tri‑axial wrist movement data. Trained on 122,640 participants across the UK, China, and the US, Sensori captures diverse movement behaviours, demographics, health axes, and physical function. In independent cohorts, the model improved disease classification for 52 of 102 conditions and incident disease risk prediction for 26 of 87 conditions, especially for neurological and psychiatric disorders.
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.
arXiv:2605. 01616v2 Announce Type: replace-cross Abstract: Human behavior is challenging to measure continuously at scale, yet traces of daily routines and well-being may be reflected in interactions with personal devices.
arXiv:2606. 14604v1 Announce Type: cross Abstract: Wearable devices and smartphones generate rich behavioural time series that can support proactive health interventions, yet systematic comparisons of modern forecasting architectures for these data are lacking.