Postural factors contributing to reaching speed and accuracy
Bibliographic record
Abstract
Objectives. Occupational reaching tasks performed with faulty postures may contribute to inefficient movement patterns that could lead to injury. Understanding relationships between posture and muscle activation during reaching tasks may elucidate movement patterns that increase occupational injury risk in workers. This study assessed whether postural factors and muscle activation predict forward reaching movement performance and accuracy. Methods. Predictor variables of forward shoulder posture (FSP), pectoral length, upper (UT), middle (MT) and lower trapezius (LT) and pectoralis major (PM) muscle activation, and UT:PM, MT:PM, and LT:PM co-activation during forward reaching were analysed for 56 individuals. Sequential linear regression equations assessed reaching variance. Results. For females, FSP, UT activation, and UT:PM co-activation explained 36% of reaction time (RT) variance, and MT:PM co-activation explained 14% of endpoint accuracy variance. For males, MT:PM co-activation explained 17% of movement time (MvT) variance, and FSP, MT:PM co-activation and MT explained 23% of accuracy variance. Conclusion. Increased co-activation was a predictor of movement performance; however, performance outcome variables differed between males (MvT) and females (RT). Muscle co-activation coupled with FSP and posterior shoulder muscle activation resulted in differences in predicting reaching performance variance. Practitioners might consider evaluating these muscle activation and postural factors in occupational reaching tasks.Trial registration: ClinicalTrials.gov identifier: NCT04944745.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".