The Influence of Load and Speed on Individuals' Movement Behavior
Bibliographic record
Abstract
Because individuals' movement patterns have been linked to their risk of future injury, movement evaluations have become a topic of interest. However, if individuals adapt their movement behavior in response to the demands of a task, the utility of evaluations comprising only low-demand activities could have limited application with regard to the prediction of future injury. This investigation examined the impact of load and speed on individuals' movement behavior. Fifty-two firefighters performed 5 low-demand (i.e., light load, low movement speed) whole-body tasks (i.e., lift, squat, lunge, push, and pull). Each task was then modified by increasing the speed, external load, or speed and load. Select measures of motion were used to characterize the performance of each task, and comparisons were made between conditions. The participants adapted their movement behavior in response to the external demands of a task (64 and 70% of all the variables were influenced [p ≤ 0.05] by changing the load and speed, respectively), but in a manner unique to the task and type of demand. The participants exhibited greater spine and frontal plane knee motion in response to an increase in speed when compared with increasing loads. However, there were a large number of movement strategies exhibited by individual firefighters that differed from the group's response. The data obtained here imply that individuals may not be physically prepared to perform safely or effectively when a task's demands are elevated simply because they exhibit the ability to perform a low-demand activity with competence. Therefore, movement screens comprising only low-demand activities may not adequately reflect an individual's capacity, or their risk of injury, and could adversely affect any recommendations that are made for training or job performance.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".