Wearable Sensor Analyses To Assess Load In Cross Country Runners
Bibliographic record
Abstract
Running biomechanics are crucial in investigating running-related injuries. Wearable technology provides a viable means of prospectively monitoring running biomechanics for preventative and prognostic purposes. Unfortunately, the use of wearable technology in research has been limited to within lab settings, or in very controlled conditions. More field-based studies are needed to validate the feasibility of using wearables to monitor biomechanics. PURPOSE: The aim of this pilot study is to determine longitudinal associations between biomechanical variables and self-reported pain across a competitive team running season, and examine the feasibility of doing so during practice sessions of varsity cross country athletes. METHODS: 7 healthy runners from the McMaster cross country team (5F) were recruited for this study. Prior to each running session, wearable sensors were attached to each leg proximal to the medial malleoli of the ankle. Sensors were worn for the entirety of a regularly scheduled practice. Following the practice participants completed a questionnaire on rate of perceived exertion (RPE), pain and pain location. Data was collected on each athlete once a week for a total of 9 sessions. Variables associated with impact (total impact load (TIL), impact load per minute (IL/min)) and asymmetry (impact asymmetry (IA), and average intensity (AI)) were assessed for correlation with measures of pain and RPE using a Pearson’s correlation (p < 0.05). RESULTS: Seven correlations between biomechanical and self-reported variables were assessed, with a number of statistically significant correlations (Table 1). Correlations between AI and pain (4/7 significant; 0.04) and TIL and pain (3/7 significant; 0.26) demonstrated the most consistently significant correlations across runners. CONCLUSIONS: Correlations between biomechanical impact variables and self-reported assessments of runners show promise as a way to longitudinally monitor athletes load. - Table 1 Subject TIL v RPE TIL v Pain IL/min v RPE IL/min v Pain IA v Pain AI v Pain 1 -0.15 0.25 -0.32 0.27 -0.17 0.62 2 0.03 -0.81 -0.05 -0.74 -0.77 -0.87 3 -0.80 -0.50 0.45 0.00 -0.16 -0.65 4 -0.27 -0.31 -0.58 0.00 0.02 -0.27 5 -0.25 -0.13 0.35 0.83 -0.06 0.88 6 -0.31 0.52 -0.38 0.33 0.34 0.18 7 0.17 0.26 -0.36 0.72 -0.61 0.81 Average -0.23 -0.10 -0.13 0.20 -0.20 0.10
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".