Association of Exposures to Seated Postures With Immediate Increases in Back Pain: A Systematic Review of Studies With Objectively Measured Sitting Time
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to conduct a systematic review of studies to determine whether sitting time measured objectively (by laboratory controlled time trial, direct observation, or wearable sensor) is associated with the immediate increase in low back pain (LBP) (determined by pain scale rating) in people >18 years of age. METHODS: Four databases (PubMed, EMBASE, SPORTDiscus, and Cumulative Index to Nursing and Allied Health Literature) were searched from inception to September 1, 2018. Randomized controlled trials and cohort and cross-sectional studies, where objectively measured sitting time was temporally matched with a measure of LBP in adults, were included. Studies without a control session conducted on a separate day were excluded. Screening, full-text review, data extraction, and risk of bias assessment (Quality In Prognosis Studies) of included papers were performed independently by 2 reviewers, with a third available to resolve disagreements. RESULTS: In total, 609 articles were identified, 361 titles/abstracts were screened,75 full-text articles were assessed for eligibility, and 10 met the inclusion criteria. All but 1 reported sitting time to be associated with an immediate increase in LBP. Six of these reported clinically relevant pain levels (n = 330). Half of the included studies were rated as having a low risk of bias and the remaining were rated as having a moderate risk of bias. CONCLUSION: Prolonged sitting increases immediate reporting of LBP in adults; however, no conclusion between sitting and clinical episodes of LBP can be made. Based upon these findings, we recommend that future prospective studies should match objectively measured sitting with temporally related pain measurements to determine whether prolonged sitting can trigger a clinical episode of LBP.
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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 | 0.018 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".