Literature DB >> 19996788

Energy cost and energy sources during a simulated firefighting activity.

Fabrizio Perroni1, Antonio Tessitore, Cristina Cortis, Corrado Lupo, Emanuele D'artibale, Lamberto Cignitti, Laura Capranica.   

Abstract

This study aimed to 1) analyze the energy requirement (VO2eq) and the contribution of the aerobic (VO2ex), anaerobic alactic (VO2al), and anaerobic lactic (VO2la-) energy sources of a simulated intervention; 2) ascertain differences in mean VO2 and heart rate (HR) during firefighting tasks; and 3) verify the relationship between time of job completion and the fitness level of firefighters. Twenty Italian firefighters (age = 32 ± 6 yr, VO2peak = 43.1 ± 4.9 mL·kg·min) performed 4 consecutive tasks (i.e., child rescue; 250-m run; find an exit; 250-m run) that required a VO2eq of 406.26 ± 73.91 mL·kg (VO2ex = 86 ± 5%; VO2al = 9 ± 3%; VO2la- = 5 ± 3%). After 30 minutes, the recovery HR (108 ± 15 beats·min) and VO2 (8.86±2.67mL·kg·min) were higher (p < 0.0001) than basal values (HR = 66 ± 8 beats·min; VO2 = 4.57 ± 1.07 mL·kg·min), indicating that passive recovery is insufficient in reducing the cardiovascular and thermoregulatory strain of the previous workload. Differences (p < 0.001) between tasks emerged for mean VO2 and HR, with a lack of significant correlation between the time of job completion and the firefighters' aerobic fitness. These findings indicate that unpredictable working conditions highly challenge expert firefighters who need adequate fitness levels to meet the requirements of their work. Practically, to enhance the fitness level of firefighters, specific interval training programs should include a wide variety of tasks requiring different intensities and decision-making strategies.

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Year:  2010        PMID: 19996788     DOI: 10.1519/JSC.0b013e3181b2c7ff

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  13 in total

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10.  Relationship between Firefighter Physical Fitness and Special Ability Performance: Predictive Research Based on Machine Learning Algorithms.

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