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Acute Exercise Impairs Cognitive Function at High Altitude

2019· article· en· W3175252684 on OpenAlexaff
Jeremy J. Walsh, Trevor J. King, P Drouin, Katrina D’Urzo, Michael E. Tschakovsky, Trevor A. Day

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsMount Royal UniversityUniversity of GuelphQueen's UniversityUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsEffects of high altitude on humansAerobic exerciseHematocritHeart rateMedicineCognitionAltitude (triangle)Hypoxia (environmental)CardiologyPhysical therapyOxygen saturationAltitude trainingInternal medicinePhysical medicine and rehabilitationBlood pressureOxygenChemistryMathematics

Abstract

fetched live from OpenAlex

Ascent to high altitude can impair aspects of cognitive function in an altitude‐dependent manner. Interestingly, a single bout of aerobic exercise transiently improves cognitive function in normobaric hypoxia, suggesting that aerobic exercise may be an effective strategy for improving brain function at altitude, but this phenomenon has yet to be explored. The purpose of this study was to investigate the effect of a single bout of aerobic exercise on cognitive function at low and high altitude in the Nepal Himalaya. Fifteen healthy volunteers (24.1±3.5yrs; 9 females) performed 20 minutes of aerobic exercise at 40–60% of their heart rate (HR) reserve at low (1400m) and high altitude (4370m) after 7 days of incremental ascent. Ascent‐related physiological data were collected in the morning before exercise including arterial blood pressure, HR, peripheral capillary oxygen saturation, hemoglobin concentration, hematocrit, end‐tidal CO 2 (P ET CO 2 ), and acute mountain sickness (AMS) scores. During exercise, participants wore HR monitors in order to maintain HR within the prescribed intensity zone. Cognitive function was assessed before and 10‐min after exercise using a battery of standardized iPad‐based tests (BrainBaseline, Digital Artefacts). A 2‐factor (Time × Condition) repeated measures analysis of variance was performed to examine differences in cognitive function before and after exercise (Time) at low and high altitude (Condition). Exploratory correlations were performed to investigate the relationship between changes in physiological measures with high‐altitude ascent and during exercise and changes in cognitive function. There was a significant interaction between altitude and exercise on a test of processing speed, working memory, and visuospatial attention, such that performance worsened following exercise at high altitude (P=0.02). In contrast, task‐switching abilities and inhibitory control remained unchanged after exercise. Higher baseline hemoglobin was associated with a decline in cognitive performance following exercise at high altitude (r= −0.58, P=0.01), whereas higher baseline P ET CO 2 was associated with improved cognitive performance following exercise at high altitude (r=0.54, P=0.03). Interestingly, there was no relationship between AMS scores and changes in cognitive function. In conclusion, acute aerobic exercise performed at high altitude impairs some measures of cognitive function, which may be impacted by physiological acclimatization during ascent. Future work should investigate the persistence of this post‐exercise decrement in cognitive function to help inform recommendations regarding decision‐making behaviors following exercise at high‐altitude. Support or Funding Information NSERC Discovery, NSERC PGS, and Wilderness Medical Society Research In‐Training Award This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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