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Record W4399688309 · doi:10.2337/db24-325-or

325-OR: Enhancing Physical Activity Engagement among Individuals with Type 1 Diabetes—Exploring Perceived Barriers in the Era of New Technologies

2024· article· en· W4399688309 on OpenAlexaboutno aff
Capucine Guédet, Sémah Tagougui, Virginie Messier, Valérie Boudreau, ANNE-SOPHIE BRAZEAU, RÉMI P.R. RABASA-LHORET

Bibliographic record

VenueDiabetes · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsType 1 diabetesMedicineInsulin pumpHypoglycemiaDiabetes mellitusPhysical activityInsulin deliveryContinuous glucose monitoringDiabetes treatmentType 2 diabetesGerontologyPhysical therapyEndocrinology

Abstract

fetched live from OpenAlex

Background: Physical activity benefits people with type 1 diabetes (PwT1D); however, PwT1D appear less active than their nondiabetic peers. This study seeks to investigate the potential of diabetes technologies in mitigating perceived barriers to physical activity in type 1 diabetes (T1D). Methods: A cross-sectional study with participants from the BETTER registry (age> 14 years) who completed BAPAD-1 (Barriers to Physical Activity in T1D) questionnaire. A significant barrier is defined by score > 5 to BAPAD-1 items. Four groups were defined according to the participants' treatment and blood glucose monitoring mode: Multiple daily injections (MDI) without continuous glucose monitoring (CGM), MDI with CGM, pump with CGM, and those using an automated insulin delivery system (AID). Results: Among 1019 eligible participants, the main perceived barrier is the fear of hypoglycemia. The mean BAPAD-1 score is similar for all groups. A higher proportion of individuals using AID systems reported significant barriers for items fear of hypoglycemia and loss of control over their diabetes compared to the No-CGM-MDI (+20% both), CGM-MDI (+7% and +10%), and CGM+Pump (+9% and +7%). Conclusion: Barriers to physical activity for PwT1D are not reduced by diabetes technologies. Some barriers are even perceived as more important for people using CGM, pump, and AID systems. Disclosure C. Guédet: None. S. Tagougui: None. V. Messier: None. V. Boudreau: None. A. Brazeau: Other Relationship; Dexcom, Inc. Research Support; Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF), Diabète québec, Fonds de recherche du Québec en Santé. 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.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.050
GPT teacher head0.309
Teacher spread0.259 · 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 designQualitative
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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