Lumbar spine movement patterns during prolonged sitting differentiate low back pain developers from matched asymptomatic controls
Bibliographic record
Abstract
BACKGROUND: Little is known about how lumbar spine movement influences mechanical changes and the potential injurious effects of prolonged flexion associated with seated postures. The purpose of this study was to examine the postural responses and pain scores of low back pain sufferers compared with asymptomatic individuals during prolonged sitting in order to understand the biomechanical factors that may be associated with sitting induced low back pain. METHOD: Sixteen participants with sitting-aggravated low back pain were age- and gender-matched with 16 asymptomatic participants. Tri-axial accelerometers were used to monitor lumbar spine angles during 90 minutes of seated computer work. Lumbar spine postures were examined using a movement pattern analysis of two types of postural adjustments, termed shifts (step-like adjustments larger than 5 degrees and fidgets (small change and return to approximately the same position). RESULTS: The LBP group reported large significant increases (P < 0.0001) in low back pain while asymptomatic individuals reported little to no pain. On average, every participant fidgeted every 40 to 50 seconds. However, only the LBP sufferers demonstrated a significant increase (P=0.04) in the number of shifts over 90 minutes of seated work; the LBP group shifted every 4 minutes in the last 30 minutes of sitting compared to every 10 minutes for the asymptomatic group. LBP sufferers also demonstrated larger amplitudes of shifts and fidgets when compared to the asymptomatic group. CONCLUSION: Greater and more frequent movement was not beneficial and did not reduce pain in individuals with pre-existing LBP. Future work to understand the biomechanical effects of proactively inducing movement may help to explain the paradox of the relationship between movement and pain.
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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 | 0.001 | 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".