Training by feel: wearable fitness-trackers, endurance athletes, and the sensing of data
Bibliographic record
Abstract
A wide range of wearable fitness-trackers are currently available that allow users to measure, monitor, visualize, and record numerous training metrics including moving pace, distance traveled, average heart rate, and calories burned. Using qualitative data collected through semi-structured interviews with amateur endurance athletes, this paper examines what individuals do with their wearable fitness-trackers and the data they produce. Drawing on the work of Deborah Lupton and Sarah Maslen, we take up the concepts of “data sensing” and the “more-than-human sensorium” to highlight the embodied and sensory dimensions of digital self-tracking. We argue that while much of the appeal of fitness-tracking technologies lies in their ability to generate objective readings of one’s performance, these devices do not supplant less quantifiable and more subjective ways of understanding one’s self. On the contrary, the participants in our study use the quantitative data generated by a fitness-tracker in conjunction with their own self-assessments to gain a more holistic sense of what they are experiencing during training or on race day. For many of our research participants, the fitness-tracker became a central part of their identity and daily routine. Most participants were reluctant to train without their fitness-trackers, even when not preparing for an event.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".