Contribution of Load Expectations to Neuromechanical Adaptations During a Freestyle Lifting Task: A Pilot Study
Bibliographic record
Abstract
OBJECTIVES: The main goal of this study was to determine to what extent load expectations modulate neuromechanical adaptations in individuals with and without chronic low back pain (cLBP) when lifting and lowering various loads. The second goal was to assess the feasibility of a simple lifting protocol during which expectations about loads were manipulated. METHODS: Seventeen participants with cLBP and 18 participants without low back pain were asked to lift and lower boxes of mild to moderate loads. Two kinds of expectations (lighter and heavier) were respectively associated to each experimental block. Self-reported exertion was assessed to control for expectations modulation. Erector spinae and vastus lateralis electromyography (EMG) activity were recorded and kinematics angle calculated. RESULTS: The results showed a main effect of expectations, with loads introduced as heavier being associated to a higher exertion compared with loads introduced as lighter. EMG activity analyses revealed significant interaction involving expectations, movement phase, and loads, as well as significant differences between groups. Kinematic angles did not reveal any significant effect of expectations nor group during the lifting phase. CONCLUSIONS: Psychological factors may contribute to neuromechanical adaptations to low back pain. Our preliminary findings show that expectations about loads may result in neuromechanical differences between individuals with cLBP and those without cLBP. This pilot study showed that testing the manipulation of expectations and EMG records was feasible but highlighted the need to go beyond single infrared markers to assess kinematics.
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".