Depth Beats Breadth: Scaling Wearable World Models of Human Physiology

Most wearable foundation models operate as static encoders, summarizing windows of observed signals into representations for downstream health prediction. We develop an action-conditioned wearable $\text{world model}$ that forecasts physiological trajectories from personal history and specified future behavior. How to scale such models is unclear: adding users and extending personal histories increase data volume differently, and longer histories also provide more inference context. Using a corpus of 5.4 million users and approximately 2 billion user-days, we study four scaling axes: population size, training context, model capacity, and inference context. Across the configurations studied, longer personal histories improve forecasting more than additional users, with much of the benefit coming from longer inference context. Larger models benefit more from long context, while scaling gains are most pronounced in event-anchored cohorts and composite physiological targets. We evaluate forecasting on 231 target–cohort–horizon combinations spanning 11 primary targets, six cohorts, and horizons of 1–180 days. Frozen model representations also support 15 downstream probes: 10 subject-level health targets, including type 2 diabetes and sleep apnea, and five forecasts of self-reported sickness or menstrual-cycle phase at different horizons. Together, these results provide a recipe for scaling wearable world models and a foundation for anticipating changes in health from longitudinal physiology and behavior.