EVALUATING THE EFFECT OF AGE AND SEX ON UPPER LIMB KINEMATICS AMONG SASKATCHEWAN FARMERS DURING FARM TASKS.
Bibliographic record
Abstract
Introduction: Musculoskeletal disorders (MSDs) are a significant concern for farmers, leading to disabilities and work-related injuries. Upper limb movements are related to injuries due to the nature of the farming activities. These disorders are associated with various risk factors associated with farming tasks. The upper extremities are particularly affected, with 75% of farmers in Saskatchewan reporting musculoskeletal complaints in these body parts. The purpose of this study is to better understand the typical movement strategies used by farmers without upper limb disorders and to evaluate the effect of age and sex on shoulder kinematics during high-risk farm tasks. Methods: Twenty farmers from rural Saskatchewan farms participated in the study (14 males and 6 females). The upper body motion of the participants was measured with inertial measurement unit sensors as they performed high-risk farm tasks such as climbing equipment, overhead drilling, shoveling grain/gravel, pruning, and lifting seed bags. The movement of their humeri and scapulae relative to the torso was tracked during these tasks, and scapular and humeral angles were calculated. Descriptive statistics were calculated for each task, including the mean, maximum, range, and standard deviation of the dependent variables, i.e., the scapular and humeral angles in each task. The effect of sex and age was assessed with linear regression. Results & Discussion: Some shoulder kinematic differences existed for sex and age in the studied farm tasks. On average, females had 20° higher humeral elevation ROM than the male farmers during climbing equipment. Age was positively associated with maximum humeral elevation (R2 = 0.210, P < 0.042) in the shoveling task and maximum dominant humeral external rotation (R2 = 0.294, P < 0.014) in the overhead drilling task. The axial rotation range of motion in overhead drilling was also associated with age (R2 = 0.328, P < 0.08). With advancing age, there are some upper limb kinematic changes in farmers during high-risk farm tasks. These findings contribute to the understanding of why MSDs occur in farmers. Conclusion: The study provides new knowledge about kinematic differences between the sexes and advancing age on the scapular and humeral movements during high-risk farm tasks. This study creates a control group, identifying a need for a larger dataset to create a normative dataset, for comparison, the initial step towards investigating the effects of injury on farm tasks, and the connection between farm work and injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".