EFFECTS OF A 12-WEEK AEROBIC TRAINING PROGRAM UTILIZING KANGOO JUMPS???
Bibliographic record
Abstract
Kangoo Jumps™ are a boot with a similar appearance to roller blade boots, with a cantilever spring system on the bottom as opposed to wheels. Kangoo Jumps™ are designed to dissipate the impact forces experienced through the ankles, knees, hips and back during running. PURPOSE To investigate whether the use of Kangoo Jumps™ provides a greater cardiovascular improvement, with fewer injuries, when compared to conventional runners in a walk/run program over a twelve-week period. METHODS A group of thirteen subjects completed a 12-week training program, consisting of 3 sessions per week, using normal running shoes (NG) (age: 28.8 ± 4.7; 75.1±25.2kg), and twelve participants using Kangoo Jumps™ (KG) (age: 25.4± 5.3; 67.4± 18.0kg), completed the same 12 week program. Peak oxygen uptake (VO2 peak) was measured pre and post training program, using a continuous treadmill protocol. All data were analyzed using ANCOVA (α = 0.05), with age being co varied; injury rates were analyzed with Chi-square 2X2 table (α = 0.05). RESULTS VO2 peak significantly increased in the KG (7.8 ± 3.5 mL/kg/min) compared with NG (1.3 ± 2.8 mL/kg/min), (p < 0.05). NG also had a significantly greater incidence of lower leg injuries when compared to KG (χ2 (1)= 6.7, p < 0.05). The NG suffered 42.8% greater lower leg injuries, which required a minimum of one-week rest from running. CONLUSION Training with Kangoo Jumps™ provides an effective means of improving aerobic capacity, while reducing the rate of injury when compared to training with normal running shoes. Supported by Jump Canada Inc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".