Arm supports for the prevention of work-related upper extremity disorders: A narrative review
Bibliographic record
Abstract
BACKGROUND: Work-related upper extremity disorders are often exacerbated by repetitive tasks and sustained non-neutral positions. These disorders significantly impact workers' quality of life, leading to absenteeism, decreased productivity, and economic burden. PURPOSE: This narrative review aims to summarize the characteristics and effectiveness of arm support technologies to prevent work-related upper extremity disorders. STUDY DESIGN: Narrative review. METHODS: Arm supports were categorized based on their design characteristics. Outcomes observed during laboratory and field studies were summarized according to previously published frameworks. RESULTS: Arm supports are categorized into static and dynamic types, with dynamic supports further divided into two-dimensional (2D) and three-dimensional (3D) systems. The mobility of 3D systems is provided by planar or pivoting armrests, or by supporting slings. Exoskeletons, a type of 3D dynamic support, provide portability as it follows the user instead of being fixed within a given workspace. The evaluation of arm supports is complex and requires a combination of quantitative and qualitative methods. Assessment should be conducted during well controlled tasks to test specific hypotheses as well as during ecologically valid use contexts to evaluate its applicability in actual work situations. Arm supports generally decrease the activity of deltoids and trapezius muscles. User acceptance is supported by perceived effort reduction and comfort, with simpler supports sometimes preferred over complex systems. Some drawbacks such as discomfort, increased antagonist muscle activity, or interference with movements can however be experienced. Implementing arm supports in workplaces involves challenges related to technology, user needs, and context. Successful integration requires considering physical and social environments, task-specific requirements, and user feedback. CONCLUSIONS: Arm supports may prevent work-related upper extremity disorders for specific individuals in selected work contexts. Further research is needed to optimize arm support technologies and develop assessment tools to identify the right worker and right context for the implementation of the right technologies.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".