Whole-Body Vibrations Associated With Alpine Skiing: A Risk Factor for Low Back Pain?
Bibliographic record
Abstract
Alpine skiing, both recreational and competitive, is associated with high rates of injury. Numerous studies have shown that occupational exposure to whole-body vibrations is strongly related to lower back pain and some suggest that, in particular, vibrations of lower frequencies could lead to overuse injuries of the back in connection with alpine ski racing. However, it is not yet known which forms of skiing involve stronger vibrations and whether these exceed safety thresholds set by existing standards and directives. Therefore, this study was designed to examine whole-body vibrations connected with different types of skiing and the associated potential risk of developing low back pain. Eight highly skilled ski instructors, all former competitive ski racers and equipped with five accelerometers and a Global Satellite Navigation System to measure vibrations and speed, respectively, performed six different forms of skiing: straight running, plowing, snow-plow swinging, basic swinging, short swinging, and carved turns. To estimate exposure to periodic, random and transient vibrations the power spectrum density (PSD) and standard ISO 2631-1:1997 parameters [i.e., the weighted root-mean-square acceleration (RMS), crest factor, maximum transient vibration value and the fourth-power vibration dose value (VDV)] were calculated. Ground reaction forces were estimated from data provided by accelerometers attached to the pelvis. The major novel findings were that all of the forms of skiing tested produced whole-body vibrations, with highest PSD values of 1.5-8 Hz. Intensified PSD between 8.5 and 35 Hz was observed only when skidding was involved. The RMS values for 10 min of short swinging or carved turns, as well as all 10-min equivalent VDV values exceeded the limits set by European Directive 2002/44/EC for health and safety. Thus, whole-body vibrations, particularly in connection with high ground reaction forces, contribute to a high risk for low back pain among active alpine skiers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".