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Record W4399685490 · doi:10.2337/db24-599-p

599-P: Automated Insulin Delivery Systems and Physical Activity—Management and Outcomes

2024· article· en· W4399685490 on OpenAlexaboutno aff
JOSÉPHINE MOLVEAU, CATHERINE L. RUSSON, Valérie Boudreau, Élisabeth Nguyen, Elsa Heyman, Jane E. Yardley, RÉMI P.R. RABASA-LHORET

Bibliographic record

VenueDiabetes · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInsulin resistanceContinuous glucose monitoringPhysical activityType 2 diabetesPost-hoc analysisInsulinAnimal sciencetar (computing)Post hocObservational studyInternal medicineEndocrinologyInsulin deliveryType 1 diabetesDiabetes mellitusPhysical therapyBiologyComputer science

Abstract

fetched live from OpenAlex

Introduction: Physical activity (PA), along with insulin adjustments and food intake for its purpose, can cause dysglycemia for people with type 1 diabetes (PWT1D), acting as a major barrier to activity. It is unclear if automated insulin delivery (AID) systems improve this situation. Methods: Observational study in real-life conditions to assess time in, below or above range (TIR, TBR [< 70mg/dl], TAR [> 180 mg/dl]) and glucose variability (CV%) during PA, 1-h post-, 4-h post-PA compared to non-PA periods with continuous glucose monitors. Participants also completed a PA log for 6 weeks including PA type (continuous, intermittent or resistance) and duration. Data were analyzed using a mixed model with Bonferroni correction for post-hoc comparisons. Results: We enrolled 22 PWT1D using AID (8 males, age: 48.7 ± 13.2 years, A1c: 6.7 ± 0.5%). Participants recorded a total of 339 PA sessions (mean 15 ± 13 [5 - 64]; 251 continuous, 59 intermittent, 29 resistance) lasting 60.9 ± 55.6 min (10 - 430 min). We found no significant effect of PA type on TIR, TBR, TAR or CV during, 1-h post or 4-h post PA. TBR as well as TAR during, 1-h and 4-h PA were comparable to non-PA periods. CV was higher in non-PA periods. All values for TBR, TAR and CV remained within optimal recommended ranges (Table). Conclusion: PWT1D using AID successfully manage PA. Respective roles of AID algorithm and adaptive behaviors need to be investigated on a larger sample size. Disclosure J. Molveau: None. C.L. Russon: None. V. Boudreau: None. E. Nguyen: None. E. Heyman: None. J.E. Yardley: Speaker's Bureau; Dexcom, Inc. Research Support; LifeScan Diabetes Institute. R.P.R. Rabasa-Lhoret: Other Relationship; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc. Research Support; Diabetes Canada. Other Relationship; Eli Lilly and Company. Research Support; Cystic Fibrosis Canada, Canadian Institutes of Health Research, FFRD - Fondation Francophone pour la Recherche du Diabète. Other Relationship; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Other Relationship; Novo Nordisk, GlaxoSmithKline plc. Consultant; HLS Therapeutics Inc., Insulet Corporation. Speaker's Bureau; CPD Networks. Other Relationship; Medtronic. Consultant; Pfizer Inc. Speaker's Bureau; Tandem Diabetes Care, Inc. Other Relationship; Sanofi. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; SFD - Société Francophone du Diabète. Funding Diabetes Canada (OG-3-21-5586-RR)

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)0.002
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.017
GPT teacher head0.298
Teacher spread0.282 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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