Effect of backpack load placement on posture and spinal curvature in prepubescent children
Bibliographic record
Abstract
Parents, educators and researchers have expressed concern about the long term impacts of children carrying excessive loads in their backpacks on a daily basis. Although many researchers have investigated appropriate weight limits for children's packs, little research has been conducted on the design of children's backpacks. The purpose of this study was to evaluate the changes in children's trunk forward lean (TFL), cranio-vertebral angle (CVA) and spinal lordosis angle (LA) that occurred with high, medium and low load locations during standing and walking. Ten-year-old children (n = 15) completed a repeated measures designed study while carrying 15% of each child's body weight in a typical backpack with only shoulder straps. A special instrumented backpack (IBP) was designed that allowed the weight to be placed in the proper location and continuously measure changes in spinal curvature. TFL and CVA postures were captured on digital video at five intervals including: standing without a backpack prior to a 1000 m walk; standing with a backpack at the beginning and end of a 1000 m walk; and walking with a backpack at the beginning and end of a 1000 m walk. Results indicated that significant changes occurred in TFL and CVA when the backpack was loaded to 15% body weight. The low load placement in the backpack produced fewer changes in CVA from the initial standing baseline measure than the high and mid placements. When all measures were assessed collectively, there were fewer changes in LA in the low load placement. These findings indicate that future backpack designs should place loads lower on the spine in order to minimize children's postural adaptations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".