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Record W3016242377 · doi:10.3390/nu12041035

Effects of Motor Mental Imagery Training on Tennis Service Performance during the Ramadan Fasting: A Randomized, Controlled Trial

2020· article· en· W3016242377 on OpenAlexaff
Sofien Fekih, Mohamed Sami Zguira, Abdessalem Koubaa, Liwa Masmoudi, Nicola Luigi Bragazzi, Mohamed Jarraya

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsYork University
Fundersnot available
KeywordsIMGAthletesMental imageMotor imageryPsychologyTraining (meteorology)Physical therapyPhysical medicine and rehabilitationMedicineComputer scienceCognition

Abstract

fetched live from OpenAlex

The objective of the present study is to analyze the effects of motor mental imagery training on tennis service performance among tennis athletes who fast during Ramadan. Participants were 38 young male tennis players, randomly divided into two groups: Imaging Training (IMG, n = 18) and control group (CG, n = 20). The CG has watched videos on the history of the Olympic Games, while IMG has followed a training program in motor imagery. The performance of the tennis service was obtained by the product between accuracy and speed of typing (accuracy × average speed of all shots (km/h)). The effect of group/time interaction (p < 0.01) was identified for all performance indicators (accuracy, running speed and performance (speed × precision)), with improvement only in IMG (p = 0.01). The results showed that motor imagery training could be an effective strategy for mitigating/counteracting the negative effects of Ramadan on the tennis service performance.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.378
Teacher spread0.330 · 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 designRandomized 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

Citations34
Published2020
Admission routes1
Has abstractyes

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