Exercise-Based Performance Enhancement and Injury Prevention for Firefighters
Bibliographic record
Abstract
Using exercise to enhance physical fitness may have little impact on performers' movement patterns beyond the gym environment. This study examined the fitness and movement adaptations exhibited by firefighters in response to 2 training methodologies. Fifty-two firefighters were assigned to a movement-guided fitness (MOV), conventional fitness (FIT), or control (CON) group. Before and after 12 weeks of training, participants performed a fitness evaluation and laboratory-based test. Three-dimensional lumbar spine and frontal plane knee kinematics were quantified. Five whole-body tasks not included in the interventions were used to evaluate the transfer of training. FIT and MOV groups exhibited significant improvements in all aspects of fitness; however, only MOV exhibited improvements in spine and frontal plane knee motion control when performing each transfer task (effect sizes [ESs] of 0.2-1.5). FIT exhibited less controlled spine and frontal plane knee motions while squatting, lunging, pushing, and pulling (ES: 0.2-0.7). More MOV participants (43%) exhibited only positive posttraining changes (i.e., improved control), in comparison with FIT (30%) and CON (23%). Fewer negative posttraining changes were also noted (19, 25, and 36% for MOV, FIT, and CON). These findings suggest that placing an emphasis on how participants move while exercising may be an effective training strategy to elicit behavioral changes beyond the gym environment. For occupational athletes such as firefighters, soldiers, and police officers, this implies that exercise programs designed with a movement-oriented approach to periodization could have a direct impact on their safety and effectiveness by engraining desirable movement patterns that transfer to occupational tasks.
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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 | 0.000 | 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 | 0.000 |
| 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".