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Record W2765930853 · doi:10.2196/resprot.8522

Tapering Practices of Strongman Athletes: Test-Retest Reliability Study

2017· article· en· W2765930853 on OpenAlexvenueno aff
Paul Winwood, Hayden J. Pritchard, Justin Keogh

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsIntraclass correlationAthletesTaperingTest (biology)Physical therapyReliability (semiconductor)MedicineDemographicsFootballPsychologyClinical psychologyDemographyPsychometricsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Little is currently known about the tapering practices of strongman athletes. We have developed an Internet-based comprehensive self-report questionnaire examining the training and tapering practices of strongman athletes. OBJECTIVE: The objective of this study was to document the test-retest reliability of questions associated with the Internet-based comprehensive self-report questionnaire on the tapering practices of strongman athletes. The information will provide insight on the reliability and usefulness of the online questionnaire for use with strongman athletes. METHODS: Invitations to complete an Internet questionnaire were sent via Facebook Messenger to identified strongman athletes. The survey consisted of four main areas of inquiry, including demographics and background information, training practices, tapering, and tapering practices. Of the 454 athletes that completed the survey over the 8-week period, 130 athletes responded on Facebook Messenger indicating that they intended to complete, or had completed, the survey. These participants were asked if they could complete the online questionnaire a second time for a test-retest reliability analysis. Sixty-four athletes (mean age 33.3 years, standard deviation [SD] 7.7; mean height 178.2 cm, SD 11.0; mean body mass 103.7 kg, SD 24.8) accepted this invitation and completed the survey for the second time after a minimum 7-day period from the date of their first completion. Agreement between athlete responses was measured using intraclass correlation coefficients (ICCs) and kappa statistics. Confidence intervals (at 95%) were reported for all measures and significance was set at P<.05. RESULTS: Test-retest reliability for demographic and training practices items were significant (P<.001) and showed excellent (ICC range=.84 to .98) and fair to almost perfect agreement (κ range=.37-.85). Moderate to excellent agreements (ICC range=.56-.84; P<.01) were observed for all tapering practice measures except for the number of days athletes started their usual taper before a strongman competition (ICC=.30). When the number of days were categorized with additional analyses, moderate reliability was observed (κ=.43; P<.001). Fair to substantial agreement was observed for the majority of tapering practices measures (κrange=.38-.73; P<.001) except for how training frequency (κ=.26) and the percentage and type of resistance training performed, which changed in the taper (κ=.20). Good to excellent agreement (ICC=.62-.93; P<.05) was observed for items relating to strongman events and traditional exercises performed during the taper. Only the time at which the Farmer's Walk was last performed before competition showed poor reliability (ICC=.27). CONCLUSIONS: We have developed a low cost, self-reported, online retrospective questionnaire, which provided stable and reliable answers for most of the demographic, training, and tapering practice questions. The results of this study support the inferences drawn from the Tapering Practices of Strongman Athletes Study.

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)0.011
metaresearch head score (Gemma)0.022
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: Protocol · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.471
GPT teacher head0.630
Teacher spread0.159 · 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
GenreProtocol

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

Citations7
Published2017
Admission routes1
Has abstractyes

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