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Cost of locomotion during loaded marching in the heat

2017· article· en· W4389025411 on OpenAlexaffabout
Hans Christian Tingelstad, Brian Kehoe, Eric Verdon, Kevin Seminuk, Tara Reilly, François Haman

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsCanadian Armed ForcesUniversity of Ottawa
Fundersnot available
KeywordsHeat stressTreadmillHeat illnessVESTRelative humidityHeart rateEnvironmental scienceAeronauticsSimulationMedicinePhysical therapyMathematicsPhysicsEngineeringMeteorologyAtmospheric sciencesStatisticsBlood pressure

Abstract

fetched live from OpenAlex

Although exercise in the heat and its effect on thermoregulatory responses has been well studied, limited information is available concerning the effect of high environmental temperatures on thermoregulatory responses, cardiovascular strain and cost of locomotion during loaded marching in the heat. Members of the armed forces are wearing protective equipment like bulletproof vests, helmets and tactical vests while performing a loaded march, often in high environmental temperatures, which could potential expose the individual uncompensable heat stress. Therefore, the purpose of this study was to investigate the effect of elevated environmental temperature on core and body temperature, heart rate and cost of locomotion in military service members during a 5 km loaded march. Participants were seven service members of the Canadian Armed Forces Light Infantry (age 24±6 years, height 178±5 cm, weight 78.8±14.7 kg, VO2max 49.2±5.6 ml·min −1 ·kg −1 , lean body mass 66.3±8.3 kg, body fat % 14.9±6.5), who performed a loaded march carrying 35 kg of equipment, under two environmental conditions ( NORMAL 21°C and 50 relative humidity (RH), and HOT 30°C and 50% RH). Treadmill speed was set to 5.17km/h and the incline was set to 1% to simulate level ground walking. The 35 kg external load consisted of military boots, uniform, helmet, fragmentation vest, tactical vest, a Colt 7 replica rubber rifle and a loaded day pack. Participants walked on the treadmill for 60 min, or until voluntary termination. Heart rate was measured using a Garmin 310xt heart rate monitor, while energy expenditure was calculated from O2 consumption and CO2 production, using an open circuit flow through metabolic system. Cost of locomotion was calculated as energy expended per kg weight (body weight and external load) per kilometer walked (J·kg −1 ·km −1 ). Core temperature was measured using a telemetric pill (Jonah Temperature Capsule). All participants completed the loaded march in the normal condition, however one participant was unable to complete the full hour in the HOT condition due to gastric distress. Heart rate was significantly higher from 25 min until the end of the loaded march when participants performed the loaded march in the HOT condition compared to NORMAL . Heart rate during the last 5 min in the HOT condition was 152±11 bpm, compared to 136.9±13.2 bpm in the NORMAL condition. Core temperature was significantly higher in the HOT condition compared to NORMAL , and was 38.2±0.5°C in the HOT condition compared to 37.7±0.3°C in the NORMAL condition after 60 min of loaded march. Cost of locomotion (J·kg −1 ·km −1 ) was also affected by environmental temperature, and was significantly higher in the HOT condition compared to NORMAL (AUC hot 165.5±10.4 vs normal 152. ±8.9). The results from this study shows that performing a loaded march at 30°C and 50% RH wearing military protective equipment lead to uncompensable heat stress, causing an increase in core temperature and heart rate (cardiovascular strain). We also observed that the uncompensable heat stress caused an increase in the cost of locomotion without changing any factors other than environmental temperature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.079
GPT teacher head0.347
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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