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Effect of health status on physiological strain during hot/humid heat exposure in older adults - The Human Heat Stress Project

2025· article· en· W4411875146 on OpenAlexaffabout
Adèle Mornas, Thomas A. Deshayes, Daniel Gagnon

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsHeat stressStrain (injury)Extreme heatBiologyStress (linguistics)Human healthPhysiologyEnvironmental healthMedicineAnimal scienceEcologyClimate changeAnatomy

Abstract

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Introduction: Greater heat-related health risks are a direct consequence of global warming. These risks are precipitated by physiological heat strain, specifically an increase in core temperature and heart rate, and sweating-induced dehydration. Older adults face greater heat-related heath risks, especially amongst those living with chronic health conditions. To our knowledge, limited research has explored the effect of health status on physiological strain of older adults during environmental heat exposure. Objectives: The objective of this study is to compare physiological heat strain between healthy older adults, older adults with risk factors for chronic diseases, and older adults living with chronic disease during exposure to a hot and humid environment with short bouts of light exercise to simulate activities of daily living. We hypothesized that physiological heat strain would be greatest for adults living with chronic disease, followed by those living with risk factors compared to healthy older adults. Methods: Seven healthy older adults (60-74 years, 5 females), 23 adults living with ≥1 risk factor according to the harmonized definition of metabolic syndrome (61-88 years, 6 females), and 10 older adults living with ≥1 non-communicable chronic disease (61-84 years, 2 females) were exposed to a 38°C environment with 60% humidity in an environmental chamber for 4 hours. To induce energy expenditure associated with activities of daily living (~3.5 METs), participants performed 10 minutes of light exercise (treadmill walking or cycling) every hour. Participants could drink water served at 15°C ad libitum. Rectal temperature and heart rate were measured continuously. Dehydration was calculated as the difference between pre- and post-exposure nude body mass (post-void) relative to pre-exposure nude body mass. Data are presented as mean ± standard deviation. Results: During the 4 hours of heat exposure, rectal temperature increased by 0.4 ± 0.3°C in healthy older adults, 0.5 ± 0.3°C in those living with at least 1 risk factor, and by 0.5 ± 0.4°C in older adults living with chronic disease. Heart rate increased by 11 ± 6 bpm in healthy older adults, 15 ± 7 bpm in those living with at least 1 risk factor, and by 9 ± 4 bpm in older adults living with chronic disease. Dehydration was 0.3 ± 1.0% in healthy older adults, 0.7 ± 0.8% in those living with at least 1 risk factor, and 0.8 ± 0.7% in older adults living with chronic disease. Conclusion: During exposure to hot/humid heat that integrates light exercise to simulate activities of daily living, these preliminary results suggest that thermal and cardiac strain do not differ markedly as a function of health status amongst older adults. However, older adults living with at last 1 risk factor or those living with chronic disease seem to experience greater dehydration. The clinical consequences of these differences remains to be determined. Canadian Institutes of Health Research This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.356
Teacher spread0.334 · 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
Published2025
Admission routes2
Has abstractyes

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