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Blood Glucose Response during Exercise in the Heat in Adults with Type 1 diabetes

2017· article· en· W2968780350 on OpenAlexafffundabout
Sheila Dervis, Martin P. Poirier, Pierre Boulay, Ronald J. Sigal, Janine Malcolm, Glen P. Kenny

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of CalgaryUniversité de SherbrookeUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsType 2 diabetesMedicineContinuous glucose monitoringInternal medicineDiabetes mellitusEndocrinologyType 1 diabetes

Abstract

fetched live from OpenAlex

Decreases in blood glucose levels during aerobic exercise are a major concern for individuals with type 1 diabetes (T1D), especially if levels drop below the normal range (hypoglycemia). However, the extent to which blood glucose may decline during a given intensity of aerobic exercise in the heat is unclear. Therefore, we examined the changes in blood glucose in individuals with T1D during intermittent exercise in the heat performed at progressively greater exercise intensities. Fifteen, physically active (VO 2peak of 44.7 ± 5.5 mL·kg −1 ·min −1 ) adults (28 ± 6 years) with T1D (hemoglobin A1c: 7.7 ± 1.1%, duration of diabetes: 13 ± 7 years) participated in the study. The participants were permitted to eat a light meal of their choice at least 90 min prior to the start of the experimental trial. Following instrumentation, participants entered a temperature controlled chamber maintained at 35°C where they rested for 30 min. Thereafter they were required to perform three successive 30 min bouts of semi‐recumbent cycling at Light (~35% of VO 2peak ), Moderate (~52% of VO 2peak ) and High intensity (~65% of VO 2peak ) levels. Each exercise bout was followed by a 30‐min recovery. Core body temperature was measured continuously. Blood glucose concentrations were measured every 15 min throughout the entire protocol. Participants ingested glucose tablets as required if blood glucose fell below pre‐specified levels. Core temperature increased gradually above baseline resting (37.10 ± 0.28°C) by 0.35± 0.28°C, 0.62± 0.28°C and 1.04± 0.34°C at the end of Light, Moderate and High intensity exercise bouts, respectively. Baseline resting glucose was 11.17 ± 3.16 mmol·L −1 and remained relatively unchanged throughout each exercise/recovery transition with the exception that blood glucose was reduced (p<0.05) during the High intensity exercise bout (7.38 ± 2.32 and 7.55 ± 3.04 mmol·L −1 at 15‐ and 30‐min of exercise) relative to the prior recovery period (9.65 ± 3.36 mmol·L −1 as measured at 30‐min of recovery) following the Moderate intensity exercise (9.05 ± 3.53 and 8.47 ± 3.71 mmol·L −1 at 15‐ and 30‐min of exercise). Finally, no changes in blood glucose occurred in the last recovery cycle following the High intensity exercise bout (7.33 ± 2.18 and 8.10 ± 2.80 mmol·L −1 at 15‐ and 30‐min of recovery). Eleven of the fifteen participants required glucose supplementation. Two out of the fifteen participants developed hypoglycemia (blood glucose <4.0 mmol·L −1 ). For one participant this occurred in the early stages of recovery following the Moderate intensity exercise (subsequently took 20 grams of glucose tabs) whereas for the second it occurred during both Moderate (subsequently ingesting 12 grams of glucose tabs) and High intensity exercise (subsequently ingesting 20 grams of glucose tabs) bouts. In conclusion, individuals with T1D performing prolonged exercise in the heat are likely to require glucose supplementation to reduce the risk of hypoglycemia, especially at higher work intensities. Support or Funding Information Funding support provided by the Canadian Institutes of Health Research (grant no. 286363, funds held by Dr. Glen P. Kenny)

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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 routes3
Has abstractyes

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