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19 Using wearable technology data to explain recreational running injury: a prospective longitudinal feasibility study

2024· article· en· W4390984684 on OpenAlexaff
Bradley Stephen Neal, Christopher Bramah, Molly McCarthy‐Ryan, Isabel S. Moore, Christopher Napier, Max R. Paquette, Allison H. Gruber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMedicineData collectionPhysical therapyInertial measurement unitGlobal Positioning SystemAnthropometryPromPhysical medicine and rehabilitationComputer scienceStatisticsInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.202
GPT teacher head0.467
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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