Digital health applications increasingly consume biometric signals from several sources at once: a wrist wearable, a chest sensor, a phone’s own sensors, and companion devices, feeding visualizations, alerts, and interventions that are only as sound as the temporal relationships among the streams. Synchronization in this setting is a stack of related but distinct problems, clock alignment across devices without shared time infrastructure, stream alignment so that samples from different sensors can be fused at analysis time, delivery with bounded latency to local and remote consumers, and state reconciliation so that intermittently connected devices converge on one record, and digital health engineering routinely solves each ad hoc, if at all. This paper develops a research concept for a low-latency biometric synchronization layer for digital health applications, specified as four cooperating services with explicit contracts. A time alignment service maintains per-device clock models against the phone’s reference using offset-and-drift estimation over exchange protocols adapted from sensor network synchronization, stamping every sample with reference-time bounds. A stream alignment service performs windowed, uncertainty-aware resampling and fusion gating, so that multi-signal features are computed only when alignment error is within declared tolerance. A delivery service streams aligned samples and derived events over persistent full-duplex channels with sequence numbering, typed backpressure, and snapshot-plus-delta resumption. A reconciliation service treats the health record as a replicated dataset with conflict-free merge semantics, guaranteeing convergence across offline periods without data loss. A phased evaluation plan measures achievable alignment error across device classes, end-to-end latency decomposition, convergence behavior under partition, and the downstream effect of alignment quality on representative multi-sensor computations. Applications, limitations, and privacy obligations are analyzed.