19 Using wearable technology data to explain recreational running injury: a prospective longitudinal feasibility study
Bibliographic record
Abstract
Introduction Running offers a 40% reduction in premature mortality risk, but high rates of musculoskeletal injury. We aimed to investigate 1) if collecting and analysing wristwatch inertial measurement unit (IMU) and global positioning system (GPS) data using a commercially-available training platform was feasible in recreational runners and 2) which variables were associated with subsequent injury. Materials and Methods We sought a minimum of 120 healthy recreational runners currently running with an IMU/GPS wristwatch. We set a priori feasibility thresholds for recruitment (maximum six-months), acceptance (minimum 80%), adherence (minimum 70%), and data collection (minimum 80%). Participants completed three patient-reported outcome measures (PROMS) detailing their psychological health, sleep quality, and intrinsic motivation to run, before linking their IMU/GPS wristwatch to the commercially-available DashLX platform. We extracted baseline anthropometric, biomechanics, metabolic, and training load data from the prior 12-weeks for analysis. Participants completed a weekly injury surveillance questionnaire to confirm their injury status over the next 12-weeks. Feasibility outcomes were analysed descriptively and between group differences with 95% confidence intervals were calculated for PROM/IMU/GPS data. Results 149 recreational runners participated. 86 participants completed the study (55 men, 31 women) and 21 became injured (0.46 injuries/1,000km). All feasibility outcomes were satisfied (recruitment=47 days; acceptance=133/149 [89%]; adherence=93/133 [70%]; data collection=86/93 [92%]). Acute load by effort was associated with subsequent injury (mean difference -562.14, 95% CI -1019.42, -21.53). Conclusion Collecting and analysing wristwatch IMU/GPS data using a commercially-available training platform was feasible in recreational runners and could be scaled up for an adequately powered prospective cohort.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".