A narrative review of running wearable measurement system accuracy and reliability: can we make running shoe prescription objective?
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Running shoe prescription is based upon outdated paradigms, foremost the idea that correcting or preventing overpronation is desirable when attempting to prevent injury.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.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".