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Record W2929113895 · doi:10.1519/jsc.0000000000003091

Evaluating the Effects of a Novel Neuromuscular Neck Training Device on Multiplanar Static and Dynamic Neck Strength: A Pilot Study

2019· article· en· W2929113895 on OpenAlexaff
Theodore H. Versteegh, James P. Dickey, Carolyn A. Emery, Lisa Fischer, Joy C. MacDermid, David M. Walton

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsMedicinePhysical medicine and rehabilitationWhiplashPlyometricsPhysical therapyStatic stretchingNeck musclesTrunkRange of motionPoison controlAnatomyPhysics

Abstract

fetched live from OpenAlex

Versteegh, TH, Dickey, JP, Emery, CA, Fischer, LK, MacDermid, JC, and Walton, DM. Evaluating the effects of a novel neuromuscular neck training device on multiplanar static and dynamic neck strength: A pilot study. J Strength Cond Res 34(3): 708-716, 2020-The neck serves an important function in damping the transference of acceleration forces between the head and the trunk, such as that occurring during contact sports or motor vehicle collisions. An inability to adequately dissipate forces has been proposed as a potential mechanism for clinical conditions such as whiplash or concussion, but current approaches to neck training may not be targeting the correct mechanisms. The purpose of this study was to explore the training effect of a novel neuromuscular strengthening protocol on dynamic and static neck strength. This was a quasiexperimental pilot study design with intervention (n = 8) and control (n = 10) groups. The intervention group was trained (twice/week, ∼10 minutes, for 7 weeks) on a training device that uses self-generated centripetal force to create a dynamic rotational resistance. This protocol is intended to target the ability of the neck muscles to perform coordinated multiplanar plyometric contractions. Both groups also continued with traditional neck strengthening that included training on a straight-plane, isotonic, 4-way neck machine. Performance on the training device showed improvement after routine practice within 1 week, as evidenced by a trend toward increased peak speed in revolutions per minute (RPM). After 7 weeks, peak RPM increased from 122.8 (95% confidence interval [CI], 91.3-154.4) to 252.3 (95% CI, 241.5-263.1). There was also a large positive effect size (Hedge's d, 0.68) in isometric composite (multiplane) neck strength favoring the intervention group over the control group (difference, 20 N; 95% CI, -8 to 48). The largest magnitude strength improvement in a single plane was in axial rotation and also favored the intervention group over the control group (Hedge's d, 1.24; difference, 46 N; 95% CI, 9-83). Future studies should explore whether the dynamic training presented here could help reduce the risk of sports concussion, whiplash, or other head-neck trauma.

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)0.002
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: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.154
GPT teacher head0.435
Teacher spread0.281 · 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 designNon-randomized trial
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

Citations16
Published2019
Admission routes1
Has abstractyes

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