Trunk Muscle Activation Patterns Differ Between Those With Low and High Back Extensor Strength During a Controlled Dynamic Task
Bibliographic record
Abstract
It is proposed that reduced function in one of the spinal systems (active, passive, and neural) outlined by Panjabi could increase the risk of experiencing a low back injury (LBI). Also proposed is that reduced function in any one system can be compensated for by adjusting the time-varying recruitment of trunk muscles. This study addressed whether those with reduced active system function (WEAK), measured as back extensor strength, would have different trunk muscle activation patterns than those with higher function (STRONG), and secondly whether this relationship would be modified following recovery from a LBI. Sixty men participated, 30 recently recovered from LBI (rLBI, 4-12 weeks post injury) and 30 who had not had a LBI in the last year (ASYM). ASYM and rLBI participants were separated into STRONG and WEAK sub-groups if their isometric back extensor strength was above or below their group median, respectively. Trunk electromyograms from 24 muscle sites were recorded during a highly controlled horizontal transfer task. Principal component analysis captured key muscle activation patterns (amplitude and temporal); then analysis of variance models tested for strength or group*strength effects on these patterns consistent with the two main objectives. Significant strength, or group by strength effects were found for 3/4 electromyographic comparisons. In general, the WEAK group required higher activation amplitudes of abdominal and back extensor muscles, and greater temporal responsiveness of back extensor muscles only to the changing external moments than those who were STRONG. Group by strength interactions found that participants in the rLBI group had greater differences between WEAK and STRONG participants for overall muscle activation amplitudes in both abdominal and back extensor muscles. This increase in muscle activation was interpreted as compensation for lower maximum force properties whereas the increased temporal responsiveness captured a greater need to modify the agonist back extensors muscle activation patterns only in response to changes in the dynamic moments. Interactions captured that the recent experience of pain (rLBI) modified the magnitude of adjustment in muscle activation patterns potentially adapting to an increased risk of instability (painful flare) events associated with a deficit (lower strength) of the active system.
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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 | 0.000 | 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) | 0.002 | 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".