The relationship between rowing-related low back pain and rowing biomechanics: a systematic review
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is common in rowers. Understanding rowing biomechanics may help facilitate prevention and improve rehabilitation. OBJECTIVES: To define the kinematics and muscle activity of rowers and to compare with rowers with current or LBP history. DESIGN: Systematic review. DATA SOURCES: EMBASE, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Web of Science and Scopus from inception to December 2019. Grey literature was searched. STUDY ELIGIBILITY CRITERIA: Experimental and non-experimental designs. METHODS: Primary outcomes were kinematics and muscle activity. Modified Quality Index (QI) checklist was used. RESULTS: 22 studies were included (429 participants). Modified QI score had a mean of 16.7/28 points (range: 15-21). Thirteen studies investigated kinematics and nine investigated muscle activity. Rowers without LBP ('healthy') have distinct kinematics (neutral or anterior pelvic rotation at the catch, greater hip range of motion, flatter low back spinal position at the finish) and muscle activity (trunk extensor dominant with less flexor activity). Rowers with LBP had relatively greater posterior pelvic rotation at the catch, greater hip extension at the finish and less efficient trunk muscle activity. In both groups fatigue results in increased lumbar spine flexion at the catch, which is greater on the ergometer. There is insufficient evidence to recommend one ergometer type (fixed vs dynamic) over the other to avoid LBP. Trunk asymmetries are not associated with LBP in rowers. CONCLUSION: Improving clinicians' and coaches' understanding of safe and effective rowing biomechanics, particularly of the spine, pelvis and hips may be an important strategy in reducing incidence and burden 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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".