The Relationship between Core Endurance, Hand Grip Strength, and Reaction Time in Young Adults
Bibliographic record
Abstract
Aim: The aim of the study was to examine the relationship between core endurance, hand grip strength, and reaction time in young adults. Method: Fifty-two undergraduate students with an average age of 21.07 (1.46) were included in this cross-sectional, observational study. Core endurance (McGill’s Core Endurance Tests), hand grip strength (Jamar Hydraulic Hand Dynamometer), and the lower extremity reaction time (OptoGait device) were assessed. Results: There was a moderate, positive correlation between the left-right trunk lateral endurance test and the right (respectively; r=0.51, r=0.47, p<0.001) and left-hand grip strength (respectively; r=0.52, r=0.51, p<0.001). A weak, negative correlation was found between the left-right trunk lateral endurance test and right lower extremity reaction time (respectively; r=-0.38, p=0.005; r=-0.39, p=0.004). There was a weak, negative correlation between left- and right-hand grip strength and right lower extremity reaction time (respectively; r=-0.32, p=0.02; r=-0.37, p=0.006). Additionally, in participants with the right dominant leg, a moderate, negative correlation was found between the right lower extremity reaction time and right-hand grip strength (r=-0.40, p=0.01) and a weak, negative correlation with left and right trunk lateral endurance test (respectively; r=-0.35, p=0.03; r=-0.33, p=0.04). Conclusion: The findings of this study, which demonstrate that there was a relationship between core endurance, hand grip strength, and reaction time, can provide a valuable resource for professionals working in this field. The association between these parameters may be helpful in guiding exercise planning in subjects such as injury, rehabilitation process, and increasing performance in 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".