A narrative review of running wearable measurement system accuracy and reliability: can we make running shoe prescription objective?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Running shoe prescription is based upon outdated paradigms, foremost the idea that correcting or preventing overpronation is desirable when attempting to prevent injury. Poor shoe prescription has the potential to affect an individual’s performance and may lead to injury and withdrawal from a potentially lifelong healthful pursuit. In this systematic narrative review, we consider the evidence (validity and reliability) for implementing two types of wearable device: instrumented ‘pressure sensing’ insoles and inertial measurement units (IMUs) to assess biomechanical data. The review summarizes existing data on the selection and placement, ability to capture kinetic and kinematic data effectively, and the limitations of both IMUs and pressure sensitive insoles for in-field measurement. We found that wearable devices have demonstrated an excellent level of reliability with some also showing good to excellent levels of validity to measure markers of potential interest in a future shoe prescription context. Further work is required to confirm which kinematic and/or kinetic measurements offer the greatest insight to individuals selecting their favoured shoe. Finally, we propose an objective alternative to the current shoe prescription rhetoric, based upon objective data collection using a wearable device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it