The Role of Preload Forces in Spinal Manipulation: Experimental Investigation of Kinematic and Electromyographic Responses in Healthy Adults
Bibliographic record
Abstract
OBJECTIVES: Previous studies have identified preload forces and an important feature of skillful execution of spinal manipulative therapy (SMT) as performed by manual therapists (eg, doctors of chiropractic and osteopathy). It has been suggested that applying a gradual force before the thrust increases the spinal unit stiffness, minimizing displacement during the thrust. Therefore, the main objective of this study was to assess the vertebral unit biomechanical and neuromuscular responses to a graded increase of preload forces. METHODS: Twenty-three participants underwent 4 different SMT force-time profiles delivered by a servo-controlled linear actuator motor and varying in their preload forces, respectively, set to 5, 50, 95, and 140N in 1 experimental session. Kinematic markers were place on T6, T7, and T8 and electromyographic electrodes were applied over paraspinal muscles on both sides of the spine. RESULTS: Increasing preload forces led to an increase in neuromuscular responses of thoracic paraspinal muscles and vertebral segmental displacements during the preload phase of SMT. Increasing the preload force also yielded a significant decrease in sagittal vertebral displacement and paraspinal muscle activity during and immediately after the thrust phase of spinal manipulation. Changes observed during the SMT thrust phase could be explained by the proportional increase in preload force or the related changes in rate of force application. Although only healthy participants were tested in this study, preload forces may be an important parameter underlying SMT mechanism of action. Future studies should investigate the clinical implications of varying SMT dosages. CONCLUSION: The present results suggest that neuromuscular and biomechanical responses to SMT may be modulated by preload through changes in the rate of force application. Overall, the present results suggest that preload and rate of force application may be important parameters underlying SMT mechanism of action.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".