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Record W4377984455 · doi:10.1093/eurjpc/zwad125.190

Impact of traffic-related air pollution on the blood pressure response to exercise in individuals with hypertension

2023· article· en· W4377984455 on OpenAlexafffundabout
Andrew Hung, Bennett Stothers, Lulu X Pei, Owen D. Harris, Cai Song, Patric Emerson Oliveira Gonçalves, Jayanth R. Arnold, Mark Gelfer, Robert J. Petrella, Chris Carlsten, Michael S. Koehle

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMedicineArterial stiffnessBlood pressurePulse wave velocityHeart rateAmbulatoryPopulationAmbulatory blood pressureAir pollutionPhysical therapyCardiologyEmergency medicineInternal medicineEnvironmental health

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Natural Sciences and Engineering Research Council of Canada and B.C. Sports & Exercise Medicine Research Foundation. Background The epidemiologic evidence suggests that the health benefits of exercise in healthy adults outweigh the adverse effects of air pollution in all but the most extreme concentrations. However, no studies have examined the acute response to exercise in air pollution in individuals with hypertension, a subgroup particularly susceptible to the cardiovascular effects of air population. Purpose The purpose of this study was to determine the impact of traffic-related air pollution on the acute cardiovascular response to exercise in patients with hypertension. We hypothesized that exposure to higher levels of traffic-related air pollution during exercise would attenuate, but not eliminate, acute post-exercise reductions in blood pressure and arterial stiffness. Methods Fourteen patients with hypertension (62.4±6.8 years; 81% male) completed a real-world, randomized, crossover study. Two 30-minute exercise bouts at 40-59% heart rate reserve were performed: once along a commercial street (high traffic) and once in an urban plaza (low traffic). Blood pressure (BP) and arterial stiffness (i.e., carotid-femoral pulse wave velocity [cfPWV]) were examined prior to, 30 minutes after, and 2 hours following exercise. 24-hour ambulatory BP monitoring was immediately completed following each visit. Black carbon, noise, relative humidity, and temperature were measured during each exercise bout (Figure 1). Results No differences were found for baseline cardiovascular measures between high and low traffic visits. At 30 minutes and 2 hours post-exercise, systolic BP was significantly reduced relative to baseline in the low-traffic condition only; diastolic BP was not significantly reduced at any timepoint. Based on linear mixed-effects analyses, exercising at the low traffic site was associated, relative to the high traffic site, with a significant (p = 0.04) reduction in systolic BP (-4.30 mm Hg [95% CI -8.09 to -0.54]) up to 2 hours following exercise after adjusting for exercise intensity, temperature, and noise; no differences were found for diastolic BP (-1.56 mm Hg [95% CI -4.87 to 1.83], p = 0.39). Each interquartile increase in black carbon (1168 ng/m³) was significantly associated with a 2.33 mm Hg (95% CI 0.37 to 4.17) increase in systolic BP up to 2 hours following exercise. No associations were observed between traffic site and BP for any ambulatory periods (i.e., 24-hour average, daytime, nighttime, or evening). The acute cfPWV response to exercise was also similar between traffic sites (p > 0.05). Conclusion Our findings suggest that exposure to traffic-related air pollution during exercise may adversely impact the beneficial short-term BP response to exercise in patients with hypertension. While the long-term implications of these changes to the acute BP response to exercise must be further explored, patients with hypertension can employ the prudent strategy of maximizing their distance from major roadways when exercising.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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