Maintaining Lumbar Spine Stability: A Study of the Specific and Combined Effects of Abdominal Activation and Lumbosacral Orthosis on Lumbar Intrinsic Stiffness
Bibliographic record
Abstract
BACKGROUND: Two potential interventions for enhancing lumbar stability are to actively increase abdominal muscle activity, either through the abdominal drawing-in maneuver (ADIM) or bracing, and passively increase lumbar stiffness using a lumbosacral orthosis (LSO). OBJECTIVE: To compare the increase in lumbar stiffness after 2 active interventions (ADIM versus bracing) and 1 passive intervention (LSO), and to evaluate the combined effect of active (abdominal bracing) and passive interventions. METHODS: In this experimental and comparative study, lumbar stiffness, a surrogate measure of lumbar stability, was estimated in 25 healthy individuals during 7 trunk perturbation conditions: (1) control, (2) ADIM, (3) bracing at 5% of right external oblique maximal voluntary activation (5% bracing), (4) bracing at 10% of right external oblique maximal voluntary activation (10% bracing), (5) LSO, (6) LSO plus 5% bracing, and (7) LSO plus 10% bracing. Electromyographic biofeedback of the external oblique was provided on a monitor, while ultrasound was used for the ADIM to ensure a sustained contraction of the transversus abdominis. RESULTS: The ADIM, 5% bracing, and 10% bracing active interventions generated comparable lumbar stiffness. However, considering that bracing can range from 10% to 20%, it may be superior to hollowing, as further estimated with a mixed-effect statistical model. Combining bracing and an LSO resulted in an additive effect on lumbar stiffness. CONCLUSION: Bracing and ADIM produced comparable lumbar stiffness, as they were performed at the same overall abdominal activation levels (5% and 10% maximal voluntary activation). The independent effects of bracing and LSO raises the possibility of combining these interventions in some circumstances. J Orthop Sports Phys Ther 2019;49(4):262-271. Epub 18 Jan 2019. doi:10.2519/jospt.2019.8565.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".