Dual foundation models for accelerometry predict future health

Wrist accelerometers are ubiquitous and capture activity, sleep, and cardiorespiratory motion, but how this relates to future disease across the phenome is unclear. We encoded one week of UK Biobank accelerometry from 97,696 participants using two frozen self-supervised models and trained a multilabel survival model on these embeddings with participant age and sex for 390 outcomes. In 5,253 held-out participants, mean concordance was 0.688. A single component explained 76% of predicted risk variance and was associated with future disease burden and mortality. Nevertheless, disease-specific scores added discrimination beyond this shared axis for 85 of 101 well-powered outcomes. A higher-powered full-cohort out-of-fold analysis identified prodromal neurodegenerative signatures, strongest for Parkinson's disease (five-year time-dependent AUROC 0.90; 428 cases), with limited attenuation after lead-time washout. Daytime movement contributed most, whereas sleep-related and genetic information contributed selectively. These findings establish one week of wrist movement as a scalable, low-cost representation of future health for wearable-based risk assessment.