The Effects of Exercise-Induced Heat Stress on Cognitive Function Assessed Using Serious Game Technology.
Bibliographic record
Abstract
Firefighting requires adequate cognition under heat stress to accurately make decisions, remain vigilant, and remember important locations within the fire scene. With emerging advancements in game technology, occupations such as the fire service have the potential to provide assessment and training tools using game-based simulations. PURPOSE: The purpose of this study was to assess aspects of cognitive function while exposed to exercise-induced heat stress using a serious game that simulates the task-level activities of an individual firefighter. METHODS: Ten male firefighters (height: 177.9 ± 1.7 cm, body mass: 89.8 ± 2.3 kg, percent body fat: 17.8 ± 1.6%, VO2peak: 44.5 ± 2.0 ml.kg-1.min-1) with a mean age of 39.4 ± 3.0 years and 15.3 ± 1.8 years of service participated in the study. Core temperature, skin temperature, and heart rate were continuously monitored and 5 mL.kg-1 of water was ingested throughout the protocol. Firefighters walked on a motorized treadmill at 4.5 km.h-1 and 2.5% grade, in a climate chamber controlled at 35 °C and 50% relative humidity for 74.4 ± 5.0 min. Cognitive function was tested using the Firefighter Task-Level serious game (FFTL), a computerized simulation of a two-story residential house fire. The FFTL was designed to incorporate 5 scenes in order to have participants complete them at differing levels of Tcore: i) scene 1 (cog 1, initial Tcore), ii) scene 2 (cog 2, 37.9°C), iii) scene 3 (cog 3, 38.2°C), iv) scene 4 (cog 4, 38.5°C), and v) scene 5 (cog 5, 37.8°C) following active cooling recovery. RESULTS: Post-hoc analyses indicated that the time to search the 2 rooms at cog 4 (61.1 ± 4.6 s) was significantly longer than room 3 (35.0 ± 5.4 s) at cog 3 but not different than room 1 or 2. Cog 5 showed a significant decrease in memory recall relative to cog 1 (-19.8 ± 6.4%). CONCLUSIONS: This study revealed that performance was not impaired during an exercise-induced heat stress protocol assessed using a serious game but room search time was prolonged at 38.5°C (cog 4). However, following an active cooling recovery regimen, memory recall was impaired compared to initial performance. The presence of long-term memory impairments may be troubling for subsequent incidents or during fire scene investigation following the emergency. This research was supported by the Workers Compensation Board of Manitoba.
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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 | 0.001 |
| 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 | 0.000 | 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".