Effects of trunk extensor muscle fatigue on repetitive lift (re)training using an augmented tactile feedback approach
Bibliographic record
Abstract
Augmented tactile and performance feedback has been used to (re)train a modified lifting technique to reduce lumbar spine flexion, which has been associated with low back disorder development during occupational repetitive lifting tasks. However, it remains unknown if the presence of trunk extensor neuromuscular fatigue influences learning of this modified lifting technique. Therefore, we compared the effectiveness of using augmented tactile and performance feedback to reduce lumbar spine flexion during a repetitive lifting task, in both unfatigued and fatigued states. Participants completed repetitive lifting tests immediately before and after training, and 1-week later, with half of the participants completing training after fatiguing their trunk extensor muscles. Both groups demonstrated learning of the modified lifting technique as demonstrated by increased thorax-pelvis coordination variability and reduced lumbar range of motion variability; however, experiencing trunk extensor neuromuscular fatigue during lift (re)training may have slight negative influences on learning the modified lifting technique. Practitioner summary: An augmented lift (re)training paradigm using tactile cueing and performance feedback regarding key movement features (i.e. lumbar spine flexion) can effectively (re)train a modified lifting technique to reduce lumbar flexion and redistribute motion to the hips and knees. However, performing (re)training while fatigued could slightly hinder learning this lifting technique.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".