The effect of visual cues at different heights on sit-to-stand movements in people with and without low back pain
Bibliographic record
Abstract
BACKGROUND: Investigating movement strategies that can be utilized to avoid pain-provocation could enhance the management of low back pain episodes. OBJECTIVE: To assess the effect of visual cues at different heights on the kinematics of sit-to-stand movements, as well as perceived difficulty and pain levels. DESIGN: Cross-over design comparing individuals with low back pain to healthy controls. METHODS: 26 asymptomatic controls and 15 individuals with chronic, recurrent low back pain performed 5 sets of 5 sit-to-stand movements. High, middle, and low visual cues were used during sets 2-4. Spinal sagittal plane range of motion, peak spinal flexion and extension angles, and trunk centre of mass velocity were obtained from kinematic data. RESULTS: The low cue led to significantly more head and lumbar spine flexion, while the high cue led to significantly more head and thoracic spine extension and increased thoracic spine range of motion. The low back pain group demonstrated a significantly lower vertical trunk centre of mass velocity than the control group during the high cue trials. There was a significant association between higher perceived difficulty scores and lower trunk centre of mass velocity for the low back pain group. Pain scores were not significantly different between cue conditions. CONCLUSION: Visual cues can be used to temporarily change the spinal kinematics of sit-to-stand movements in people with and without low back pain. This could be helpful in clinical practice to encourage more, or less, movement in specific spinal regions, and avoid pain provocation to facilitate functional rehabilitation.
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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 | 0.004 |
| 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 | 0.001 | 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".