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Record W4297294420 · doi:10.3389/fendo.2022.953879

Prevalence of nocturnal hypoglycemia in free-living conditions in adults with type 1 diabetes: What is the impact of daily physical activity?

2022· article· en· W4297294420 on OpenAlexafffund
Joséphine Molveau, Rémi Rabasa‐Lhoret, Étienne Myette‐Côté, Virginie Messier, Corinne Suppère, Kathryn J. Potter, Elsa Heyman, Sémah Tagougui

Bibliographic record

VenueFrontiers in Endocrinology · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsUniversity of Prince Edward IslandUniversité de MontréalMontreal Clinical Research Institute
FundersInstitut de Recherche Clinique De Montréal
KeywordsHypoglycemiaNocturnalMedicineType 1 diabetesGlycemicMealDiabetes mellitusInsulinType 2 diabetesInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Objective: Studies investigating strategies to limit the risk of nocturnal hypoglycemia associated with physical activity (PA) are scarce and have been conducted in standardized, controlled conditions in people with type 1 diabetes (T1D). This study sought to investigate the effect of daily PA level on nocturnal glucose management in free-living conditions while taking into consideration reported mitigation strategies to limit the risk of nocturnal hyoglycemia in people with T1D. Methods: : 7.6 ± 0.8%), 20-60 years old, living with T1D, were collected. One week of continuous glucose monitoring and PA (assessed using an accelerometer) were collected in free-living conditions. Nocturnal glucose values (midnight-6:00 am) following an active day "ACT" and a less active day "L-ACT" were analyzed to assess the time spent within the different glycemic target zones (<3.9 mmol/L; 3.9 - 10.0 mmol/L and >10.0 mmol/L) between conditions. Self-reported data about mitigation strategies applied to reduce the risk of nocturnal hypoglycemia was also analyzed. Results: Only 44% of participants reported applying a carbohydrate- or insulin-based strategy to limit the risk of nocturnal hypoglycemia on ACT day. Nocturnal hypoglycemia occurrences were comparable on ACT night versus on L-ACT night. Additional post-meal carbohydrate intake was higher on evenings following ACT (27.7 ± 15.6 g, ACT vs. 19.5 ± 11.0 g, L-ACT; P=0.045), but was frequently associated with an insulin bolus (70% of participants). Nocturnal hypoglycemia the night following ACT occurred mostly in people who administrated an additional insulin bolus before midnight (3 out of 5 participants with nocturnal hypoglycemia). Conclusions: Although people with T1D seem to be aware of the increased risk of nocturnal hypoglycemia associated with PA, the risk associated with additional insulin boluses may not be as clear. Most participants did not report using compensation strategies to reduce the risk of PA related late-onset hypoglycemia which may be because they did not consider habitual PA as something requiring treatment adjustments.

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)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.289
Teacher spread0.277 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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