Using wearable technology data to explain recreational running injury: A prospective longitudinal feasibility study
Bibliographic record
Abstract
OBJECTIVES: 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. DESIGN: Prospective longitudinal cohort. PARTICIPANTS: Healthy recreational runners. MAIN OUTCOME MEASURES: 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. We extracted baseline anthropometric, biomechanical, metabolic, and training load data from their IMU/GPS wristwatch for analysis. Participants completed a weekly injury status surveillance questionnaire over the next 12-weeks. Feasibility outcomes were analysed descriptively and injured versus non-injured group differences with 95% confidence intervals were calculated for PROM/IMU/GPS data. RESULTS: 149 participants consented; 86 participants completed (55 men, 31 women); 21 developed an injury (0.46 injuries/1000km). Feasibility outcomes were satisfied (recruitment = 47 days; acceptance = 133/149 [89%]; adherence = 93/133 [70%]; data collection = 86/93 [92%]). Acute load by calculated 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.
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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 | 0.011 |
| 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) | 0.002 | 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".