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83 Heart rate recovery changes following repetitive head impacts in Canadian football athletes

2025· article· en· W4410952793 on OpenAlexaffabout
Géraldine Martens, Bertrand R. Caré, Johan Merbah, Sophie-Andrée Vinet, Samuel Guay, Raphaëlle Creniault, Amélie Apinis-Deshaies, Laurie‐Ann Corbin‐Berrigan, Éric Wagnac, Miriam H. Beauchamp, François Prince, Louis De Beaumont

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineÉcole de Technologie SupérieureUniversité du Québec à Trois-RivièresInnovation and Economic Development Trois RivièresUniversité du QuébecUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsFootballAthletesHead (geology)Football playersHeart ratePhysical medicine and rehabilitationMedicinePhysical therapyInternal medicineHistoryBiologyBlood pressure

Abstract

fetched live from OpenAlex

Purpose Subconcussive head impacts during contact sports are challenging to identify and their consequences remain elusive. Impaired autonomic nervous system responses involving altered cardiac dynamics have been reported following mild traumatic brain injury but remain underexplored in contact sports. This study monitored online effects of head impact exposure on athletes’ cardiovascular function during a Varsity Canadian football season.Methods Heart rate recovery (HRR) segments (n=225) were extracted from 11 skill players (median [IQR] age: 23 [2] years; body mass index: 26 [1] kg/m²) equipped with two wearable sensors recording accelerations, heart rate, velocity, estimated metabolic energy expenditure, and fast head acceleration events (HAE). The heart signal derivative was used to detect HRR segments which were then matched with the number and intensity of HAE previously sustained during the game.Results Following a single HAE above 40 g, the median HRR (ΔHR/Δt=0.461 bpm/s, n=23 segments) decreased of 19% (p=0.005) compared to segments prior to the event (ΔHR/Δt=0.569 bpm/s, n=182 segments). This reduction was also observed with HAE as low as 20 g (0.474 bpm/s, n=42 segments; 30% reduction; p= 4.30e-08, figure 1). These were neither correlated with metabolic energy expenditure (R²=0.009, p=0.541) nor with peak running velocity reached before HAE (R²=4e-04, p=0.896).Abstract 83 Figure 1Heart rate recovery rates for segments binned by upper bounds for peak linear acceleration of 10 g (i.e., no head acceleration event – HAE –, leftmost), up to 20 g (second box), up to 30 g (third box), up to 40 g (fourth box) and 40 g and up (last box on the right). Significance test: Kruskal-Wallis rank sum test relative to the «no impacts» sample, thresholds: * = 0.05, ** = 0.01, *** = 0.001Conclusion This demonstrates a possible direct relationship between HAE starting at 20 g and disturbed cardiac response. Given the repetitive nature of subconcussive hits beyond this ≥ 20 g threshold, there is an urgent need to investigate whether or how altered heart rate kinetics could influence severity of head injury in contact sports.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.403
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.364
Teacher spread0.312 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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