The one-week and three-month reliability of acceleration outcomes from an insole-embedded inertial measurement unit during treadmill running
Bibliographic record
Abstract
Inertial measurement units (IMUs) represent an exciting opportunity for researchers to broaden our understanding of running-related injuries, and for clinicians to expand their application of running gait analysis. The primary aim of our study was to investigate the 1-week (short-term) and 3-month (long-term) reliability of peak resultant, vertical, and anteroposterior accelerations derived from insole-embedded IMUs. The secondary aim was to assess the reliability of peak acceleration variability and left–right limb symmetry in all directions over the short and long term. A sample of healthy adult rearfoot runners (n = 23; age 41.7 ± 11.2 years) ran at a variety of speeds (2.5 m/s, 3.0 m/s, and 3.5 m/s) on a treadmill in standardised footwear with insole-embedded IMUs in each shoe. Peak accelerations exhibited good to excellent short-term reliability and moderate to excellent long-term reliability in all directions. Peak acceleration variability showed poor to good short- and long-term reliability, whereas the symmetry of peak accelerations demonstrated moderate to excellent and moderate to good short- and long-term reliability, respectively. Our results demonstrate how insole-embedded IMUs represent a viable option for clinicians to measure peak accelerations within the clinic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".