Many experienced runners consider the use of wearable devices an important element of the training process. A key techno-utopic promise of wearables lies in the use of proprietary algorithms to identify training load errors in real-time and alert users to risks of running-related injuries. Such real-time `knowing` is claimed to obviate the need for athletes` subjective judgements by telling runners how they have deviated from a desired or optimal training load or intensity. This realist-contoured perspective is, however, at odds with sociological research indicating that users of wearables engage in active `data sense-making` that is highly contextualised. To investigate how athletes use (or not) algorithmic analysis to understand, make sense of, and improve their performance in real-time, we undertook qualitative interviews with distance runners to explore lived experiences of running with wearables. The runners described how they actively interpreted data from wearables, drawing on their own experience, `somatic knowledge`, and embodied ways of knowing. This allowed them to assess the relevance and usefulness of data in relation to their own goals, intentions, and feelings. Our findings challenge the techno-utopic promises of real-time and predictive analytics.
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