Comparison of the Spinal Loads Produced by Carrying a Backpack and Carrying a Person Piggyback: Crossfit Training
Bibliographic record
Abstract
Background of Study: The piggyback carry has recently become a more popular exercise through the emerging sport of CrossFit. Purpose: The purpose of this study was to determine any biomechanical differences that exist in the lumbar spine when carrying no load, a backpack, and a person on the back. Methods: Twelve 70+ kg male strength-trained athletes were recruited from local CrossFit affiliates. One child with a mass of 27 kg was recruited to be the piggybackpassenger for all participants. All participants and the guardian of the passenger signed an informed consent form. The participants walked three times over a force plate for each of three conditions: carrying no load, a 27 kg backpack, or a 27 kg passenger. Three Canon video cameras recorded each trial, and Dartfish Software was used to measure joint angles and gait parameters. Maximal trunk inclination angle, was used in a static lumbar spine model to calculate trunkmuscle torque and force, and lumbosacral joint reaction forces. Results: Both load conditions produced compensatory trunk flexion; trunk flexion increased from no load to piggybacking to backpacking. Mean values were determined for each participant for each variable, and these values were compared amongst the three conditions of no load, piggybacking, and backpacking. An alpha value of 0.05 was used. Conclusion: Due to the more extreme position of the trunk andgreater magnitude of torques, backpacking likely places the musculoskeletal system at more risk than does piggybacking.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".