Shoulder pain influences kinematics during farm work tasks: An in-field study
Bibliographic record
Abstract
Background Work-related musculoskeletal disorders (MSDs) are prevalent among agricultural producers. Upper limb MSDs, especially in the shoulder and neck, are common, yet research on their development and prevention is limited. This study aims to investigate the influence of shoulder pain, age, and sex on shoulder kinematics during farm work tasks. Methods Farmers in Saskatchewan were recruited and divided into groups with and without shoulder pain. Participants performed four tasks (Overhead Drill, Climb Seeder, Seed Bag Lift, Shovel) while wearing inertial measurement units (IMUs) to track humeral and scapular movements. Data were analyzed using linear regression and Kruskal-Wallis tests (p < .05) to assess the effects of pain, age, and sex on shoulder kinematics. Results Forty-two participants (23 without pain, 19 with pain) completed the study. Pain significantly influenced shoulder kinematics during the Overhead Drill, Seed Bag Lift, and Shovel tasks. During the Overhead Drill, the pain group exhibited higher scapular upward rotation (p = .04, +5.1°) and females showed lower maximal humeral elevation (p = .049, −11.7°). In the Seed Bag Lift, the pain group had lower scapular upward rotation (p = .012, −18.7°) and higher humeral internal rotation (p = .04, +12.0°). Humeral elevation was also lower in the pain group during the Shovel task (p = .019, −12.7°). Conclusions Shoulder pain affects shoulder kinematics in farm work tasks, with variations depending on the task. Pain-related compensations can be both protective and harmful. These findings highlight the potential risk for shoulder injury in many aspects of farm work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".