The force exerted during manual tasks is a dominant risk factor for upper-limb musculoskeletal disorders. To identify tasks that may lead to fatigue over a shift, or increase the risk of injury, the demands placed on the hand and forearm system must be quantified and predicted. The purpose of this research was to determine how different ways of simulating manual tasks affected the estimate of demand on the hand and forearm and how well normative data could be used to provide an estimate of that demand. The forces and moments required to perform 20 manual tasks were measured and simulations with three different levels of realism developed, ranging from simple feedback, with real parts, postures and timing to more controlled simulations with simplified parts, standard postures and 5s static exertions. 11 workers hired from a temporary employment agency each performed the simulated tasks and their physical demand was determined using perceived effort, the muscle activity of 8 hand and forearm muscles, and grip (or pinch) force matching. Based on these criteria, the best simulation was that with the same handle size, shape and orientation as the criterion version of the task using simple feedback to match one or two forces. Over the variety of tasks studied here, perceived effort, grip force matching and extensor digitorum activation provided the most similar demand estimate to the criterion task of all measured parameters. The more controlled simulation had the highest correlation compared with normative demand. Overall, the more changes in hand-object interface made between the task of interest and a simulation or normative data, the greater the discrepancy in demand. Normative data tended to underestimate demand, thus underestimating the risk of fatigue and injury. The use of simulations and task specific normative data to estimate hand task demand, with an accuracy useful for field measurements by ergonomists, was supported.

The force exerted during manual tasks is a dominant risk factor for upper-limb musculoskeletal disorders. To identify tasks that may lead to fatigue over a shift, or increase the risk of injury, the demands placed on the hand and forearm system must be quantified and predicted. The purpose of this research was to determine how different ways of simulating manual tasks affected the estimate of demand on the hand and forearm and how well normative data could be used to provide an estimate of that demand. The forces and moments required to perform 20 manual tasks were measured and simulations with three different levels of realism developed, ranging from simple feedback, with real parts, postures and timing to more controlled simulations with simplified parts, standard postures and 5s static exertions. 11 workers hired from a temporary employment agency each performed the simulated tasks and their physical demand was determined using perceived effort, the muscle activity of 8 hand and forearm muscles, and grip (or pinch) force matching. Based on these criteria, the best simulation was that with the same handle size, shape and orientation as the criterion version of the task using simple feedback to match one or two forces. Over the variety of tasks studied here, perceived effort, grip force matching and extensor digitorum activation provided the most similar demand estimate to the criterion task of all measured parameters. The more controlled simulation had the highest correlation compared with normative demand. Overall, the more changes in hand-object interface made between the task of interest and a simulation or normative data, the greater the discrepancy in demand. Normative data tended to underestimate demand, thus underestimating the risk of fatigue and injury. The use of simulations and task specific normative data to estimate hand task demand, with an accuracy useful for field measurements by ergonomists, was supported.

Objectif : La perception de l’effort (PE) est utilisée dans la prescription et la supervision d’exercice lors de tâches locomotrices et de résistance. Néanmoins, son utilisation pour prescrire et superviser l’exercice lors de tâches motrices des membres supérieurs reste incertaine. Cette étude vise à tester ces possibilités. Méthodes : Quarante participants se sont portés volontaires. Dans l’expérience 1, quatre intensités de PE ont été utilisées pour prescrire l’exercice dans une version modifiée du Test du Box and Block (TBB) et d’une tâche de pointage. La possibilité de superviser l’exercice a été étudiée en imposant trois niveaux de difficultés via un tempo ou un poids et en mesurant les changements de PE associés. L’expérience 2 réplique la possibilité de prescrire l’exercice avec l’intensité de la PE et étudie les effets de l’ajout d’un poids sur l’avant-bras dominant sur la performance et la PE au cours de la version standardisée du TBB. L’activité musculaire, les fréquences cardiaque et respiratoire ont été mesurées. Résultats : Dans l’expérience 1, l’augmentation de l’intensité de la PE pour prescrire l’exercice a induit une augmentation de la performance et l’augmentation de la difficulté des tâches a augmenté la PE du participant. Dans l’expérience 2, la possibilité d’utiliser la PE pour prescrire l’intensité de l’exercice a été répliquée. La réalisation du TBB avec un poids additionnel révèle un maintien de la performance au prix d’une PE plus élevée. Dans les deux expériences, l’activité musculaire constitue le meilleur corrélat physiologique de la PE. Conclusion : Nos résultats suggèrent que la PE est un outil efficace pour prescrire et superviser l’exercice au cours de tâches motrices des membres supérieurs. / Purpose: While the perception of effort (PE) is widely used to prescribe and monitor exercise during locomotor and resistance tasks, its use to prescribe and monitor exercise during upper-limb motor tasks remains unclear. This study aimed to test these possibilities. Methods: Forty participants volunteered in two experiments. In experiment 1, by using a modified version of the box and block test (BBT) and a pointing task, four PE intensities were used to prescribe the exercise. The possibility of monitoring the exercise was investigated during these tests by monitoring changes in the rating of PE in response to three task difficulties manipulated with different movement tempo and weights added on the exercising forearm. Experiment 2 replicated the possibility of prescribing the exercise with the PE intensity during the BBT and explored the impact of adding weight on the exercising forearm on performance and PE during the standardized version of the BBT. Muscle activation, heart rate and respiratory frequencies were recorded. Results: In experiment 1, increasing the PE intensity to prescribe the exercise induced an increased performance between each intensity. Increasing task difficulty with higher movement tempo and adding weight on the forearm increased the rating of PE. Experiment 2 replicated the possibility to use PE intensity for exercise prescription during the BBT. When completing the BBT with an additional weight on the forearm, performance was maintained at a cost of a higher PE. In both experiments, muscle activation was the best physiological marker of PE. Conclusion: Our results suggest that PE is an efficient tool to prescribe and monitor exercise during upper-limb motor tasks.