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Treadmill running is a common workout for individuals across fitness levels. In this paper, we propose mm-RunAssist, a first-of-its-kind mmWave-based system that enhances treadmill workouts by monitoring respiration waveforms, running rhythm (i.e., coordination between breathing and strides), and detecting fall-off events. Extracting respiration from moving subjects using RF signals is challenging due to dominant motion artifacts. While prior deep learning efforts use adversarial or contrastive learning to mitigate such artifacts, they have been evaluated primarily under low-intensity activities like walking. To address this gap, mm-RunAssist introduces a Dual-task Variational U-Net that shares latent representations between respiration and upper-body movement tracking. This dual-task setup, guided by belt and depth sensors during training, improves reconstruction under intense body motion. Our system not only recovers fine-grained respiratory patterns during running but also supports cadence analysis through arm swing tracking. Extensive experiments with three state-of-the-art baselines under various conditions demonstrate mm-RunAssist's robustness and accuracy in treadmill running scenarios. Results show that mm-RunAssist advances RF sensing by effectively extracting vital signs even during vigorous body movements, offering new capabilities for fitness monitoring and non-intrusive health assessment.