THE EFFECT OF SPINAL STABILISATION TRAINING ON SPINAL MOBILITY, VERTICAL JUMP, AGILITY, AND BALANCE
Bibliographic record
Abstract
In a population of patients with chronic low back pain (CLBP), spinal stabilization training has been shown to relieve symptoms and improve range of motion of the hips and lumbar spine. However, no study has yet shown that this type of training improves the stability of the spine in either healthy or CLBP patients. Furthermore, while stabilization training is used by athletes across a diversity of sport backgrounds, a correlation between spinal stability and athletic performance has not been quantified. PURPOSE To determine if spinal stability can be enhanced in a group of college-aged athletes and to quantify a possible relationship between spinal stability and athletic performance. METHODS 36 subjects (20 +/- 1.2 yrs) were randomly assigned to either one of two treatment groups and a control group. Over a ten-week period members of treatment group A performed spinal stabilization training exercises 4 days per week while members of treatment group B performed an equivalent volume of traditional abdominal exercises. The control group performed no exercise in addition to their specific sport training. At weeks 0, 5, and 10 spinal stability, vertical jump, agility, and balance were assessed. Comparisons were made using ANOVA for repeated measures. RESULTS Improvements in spinal stability were greater for group A when compared with the improvements of either group B or the control (10 +/- 1.25 vs. 3 +/- 1 vs. 2.5 +/- 0.75 mmHg, p < 0.05). Greater improvements were also observed in group A for agility (0.57 vs. 0.23 vs. 0,20 sec., p < 0.05) and balance (18 vs. 12 vs. 11 sec, p < 0.05) tests. However, no difference was noted between groups for the vertical jump assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".