Vergleich von kinematischen und kinetischen Parametern zwischen den Bewegungsformen Nordic Walking, Walking und Laufen
Bibliographic record
Abstract
Based on a higher cardio-pulmonary and cardio-vascular benefit and a promised reduction of mechanical load of the musculoskeletal system Nordic Walking (NW) shows an increased market potential. The present study should investigate whether there are biomechanical differences between the locomotion patterns NW, walking and running. Moreover possible resultant load differences should be determined. Eleven subjects, who were already experienced with the NW-technique, participated in this experiment. The kinematic data were collected using two high-speed camera systems from posterior and from lateral at the same time. Simultaneously the ground reaction forces were recorded. The kinematic and the kinetic data reveal differences between the three analyzed locomotion patterns. For NW as well as walking the mechanical load of the lower extremity is lower compared to running. None of the kinematic parameters suggest a "physiological benefit" of NW compared to walking. Moreover NW shows higher vertical and horizontal forces during landing. Exclusively the lower vertical force peak during push off indicates a lower mechanical load for NW in comparison to walking. Consequently it is questionable is NW -- based on its promised "biomechanical benefits" compared to walking -- should be still recommended for overweight people and for people with existing musculoskeletal problems of the lower limb.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".