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Record W2994936198 · doi:10.1080/09603123.2019.1699506

Associations between air pollution and cardio-respiratory physiological measures in older adults exercising outdoors

2019· article· en· W2994936198 on OpenAlexafffund
David M. Stieb, Robin Shutt, Lisa Marie Kauri, Sarah A. Mason, Li Chen, Mieczysław Szyszkowicz, Nina A. Dobbin, Marc Rigden, Branka Jovic, Marie Mulholland, Martin S. Green, Ling Liu, Guillaume Pelletier, Scott Weichenthal, Robert Dales, Julie Andrade, Isaac Luginaah

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcGill UniversityResponse Biomedical (Canada)Western UniversityUniversity of OttawaHealth Canada
FundersHealth Canada
KeywordsInterquartile rangeCardiorespiratory fitnessHeart rate variabilityMedicineHeart rateEnvironmental healthAir pollutionPhysical therapyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

We examined whether exercising indoors vs. outdoors reduced the cardio-respiratory effects of outdoor air pollution. Adults ≥55 were randomly assigned to exercise indoors when the Air Quality Health Index was ≥5 and outdoors on other days (intervention group, n = 37), or outdoors everyday (control group, n = 35). Both groups completed cardio-respiratory measurements before and after exercise for up to 10 weeks. Data were analyzed using linear mixed effect regression models. In the control group, an interquartile range increase in fine particulate matter (PM2.5) was associated with increases of 1.4% in heart rate (standard error (SE) = 0.7%) and 5.6% (SE = 2.6%) in malondialdehyde, and decreases of 5.6% (SE = 2.5%) to 16.5% (SE = 7.5%) in heart rate variability measures. While the hypothesized benefit of indoor vs. outdoor exercise could not be demonstrated due to an insufficient number of intervention days (n = 2), the study provides evidence of short-term effects of air pollution in older adults. ISRCTN #26552763.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.110
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.155
GPT teacher head0.435
Teacher spread0.279 · 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 teacher head, 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".

Quick stats

Citations10
Published2019
Admission routes2
Has abstractyes

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