Effects of slackline training on core endurance and dynamic balance
Bibliographic record
Abstract
Slackline challenges balance by walking on a tensioned strap, where the trunk muscles help to maintain or regain \nbalance. This study aimed to compare a cohort of individuals who regularly practiced slackline and physically inactive \nindividuals on core endurance (CE) and dynamic balance (DB) and to determine whether CE is associated with years of \nslackline practice. Nine individuals (7 men) who practiced slackline regularly (SG; age= 24.5±3.6 years) and nine physically \ninactive individuals (7 men) (CG; age= 23.2±3.3 years) were compared. CE was assessed with the McGill battery (trunk \nflexor, extensor, and side-bridge test) and the plank test. DB was measured with the modified star excursion balance test in \nstable and unstable conditions. The SG maintained a 36.2% and 45% longer time in left lateral bridge (p=.049) and plank \n(p=.031), respectively, compared to the CG. The distance achieved in the stable DB test was similar between groups, but in \nunstable condition was 37.8% greater (p=.016) in SG in both legs and 46.6% greater in the non-dominant leg (p=.039) \ncompared to CG. The SG showed a correlation between years of slackline practice and flexor (r=.674; p=.046), right lateral \n(r=.765; p=.016) and left (r=.730; p=.026) trunk endurance. In conclusion, those who practice slackline maintain a longer \ntime in the plank and left lateral bridge test and achieve a higher reach distance in unstable DB compared to physically inactive \nindividuals who do not practice slackline.
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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 | 0.000 | 0.001 |
| 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".